The e-Advocate Quarterly Magazine Isaiah 1:17 | 1 John 3:16 James 2:16-16 | Luke 4:18-19
American Poverty In The New Millennium
“Helping Individuals, Organizations & Communities Achieve Their Full Potential”
Vol. XII, Issue LII – Q-1 January| February| March 2026
The Advocacy Foundation, Inc. Helping Individuals, Organizations & Communities Achieve Their Full Potential
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Dedication ______
Every publication in our many series‘ is dedicated to everyone, absolutely everyone, who by virtue of their calling, by Divine inspiration, direction and guidance, is on the battlefield dayafter-day striving to follow God‘s will and purpose for their lives. And this is with particular affinity for those Spiritual warriors who are being transformed into excellence through daily academic, professional, familial, and other challenges. We pray that you will bear in mind: Matthew 19:26 (NIV) Jesus looked at them and said, "With man this is impossible, but with God all things are possible." (Emphasis added) To all of us who daily look past our circumstances, and naysayers, to what the Lord says we will accomplish: Blessings!
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The Advocacy Foundation, Inc.
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The Advocacy Foundation, Inc. Helping Individuals, Organizations & Communities Achieve Their Full Potential
The e-Advocate Quarterly
American Poverty In The New Millennium
“Helping Individuals, Organizations & Communities Achieve Their Full Potential
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John C Johnson III Founder & CEO
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Biblical Authority ______
Isaiah 1:17 (NIV) 17
Learn to do right; seek justice. Defend the oppressed.
Take up the cause of the fatherless; plead the case of the widow.
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1 John 3:17 (NIV) 17
If anyone has material possessions and sees a brother or sister in need but has no pity on them, how can the love of God be in that person?
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James 2:15-16 (NIV) 15
Suppose a brother or a sister is without clothes and daily food. 16 If one of you says to them, ―Go in peace; keep warm and well fed,‖ but does nothing about their physical needs, what good is it?
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Luke 4:18-19 (NIV) 18
―The Spirit of the Lord is on me, because he has anointed me to proclaim good news to the poor.
He has sent me to proclaim freedom for the prisoners and recovery of sight for the blind, to set the oppressed free, 19
to proclaim the year of the Lord‘s favor.‖
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Table of Contents The e-Advocate Quarterly American Poverty In The New Millennium
Biblical Authority I.
Introduction
II.
Measuring Poverty Rates
III.
The Demographics of Poverty in the US
IV.
The Effectiveness of Education
V.
Risk Factors that Lead to Poverty
VI.
Protective Factors that Alleviate Poverty
VII. Income Inequality VIII. The De-Industrialization Crisis IX.
The War on Poverty in the US
X.
The Robin Hood Foundation (New York, NY)
XI.
The Tipping Point Community (San Francisco, CA)
XII. References Attachments A. Poverty in America: Trends and Explanations B. The Paradox of Poverty in America C. The Poverty and Inequality Report
Copyright Š 2015 The Advocacy Foundation, Inc. All Rights Reserved.
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Introduction Poverty is a state of deprivation, or a lack of the usual or socially acceptable amount of money or material possessions. The most common measure of poverty in the U.S. is the "poverty threshold" set by the U.S. government. This measure recognizes poverty as a lack of those goods and services commonly taken for granted by members of mainstream society. The official threshold is adjusted for inflation using the consumer price index. The government's definition of poverty is based on total income received and does not include non-cash supplements such as food stamps or public housing. For example, the poverty level for 2014 was set at $23,850 (total yearly income) for a family of four.
Most Americans will spend at least one year below the poverty line at some point between ages 25 and 75. Poverty rates are persistently higher in rural and inner city parts of the country as compared to suburban areas. In November 2012 the U.S. Census Bureau said more than 16% of the population lived in poverty, including almost 20% of American children, up from 14.3% (approximately 43.6 million) in 2009 and to its highest level since 1993. In 2008, 13.2% (39.8 million) Americans lived in poverty. Starting in the 1980s, relative poverty rates have consistently exceeded those of other wealthy nations. California has a poverty rate of 23.5%, the highest of any state in the country. In 2009 the number of people who were in poverty was approaching 1960s levels that led to the national War on Poverty. In 2011 extreme poverty in the United States, meaning households living on less than $2 per day before government benefits, was double 1996 levels at 1.5 million Page 12 of 102
households, including 2.8 million children. This would be roughly 1.2% of the U.S. population in 2011, presuming a mean household size of 2.55 people. Recent census data shows that half the population qualifies as poor or low income, with one in five Millennials living in poverty. Academic contributors to The Routledge Handbook of Poverty in the United States postulate that new and extreme forms of poverty have emerged in the U.S. as a result of neoliberal structural adjustment policies and globalization, which have rendered economically marginalized communities as destitute "surplus populations" in need of control and punishment.
In 2011, child poverty reached record high levels, with 16.7 million children living in food insecure households, about 35% more than 2007 levels. A 2013 UNICEF report ranked the U.S. as having the second highest relative child poverty rates in the developed world. There were about 643,000 sheltered and unsheltered homeless people nationwide in January 2009. Almost two-thirds stayed in an emergency shelter or transitional housing program and the other third were living on the street, in an abandoned building, or another place not meant for human habitation. About 1.56 million people, or about 0.5% of the U.S. population, used an emergency shelter or a transitional housing program between October 1, 2008 and September 30, 2009. Around 44% of homeless people are employed.
Public Attitudes The growth of inequality has provoked a political protest movement – The Occupy Movement – starting in Wall Street and spreading to 600 communities across the United States in 2011. Its main political slogan – "We are the 99%" – references its dissatisfaction with the concentration of income in the top 1%. A December 2011 Gallup poll found a decline in the number of Americans who felt reducing the gap in income and wealth between the rich and the poor was extremely or very important (21 percent of Republicans, 43 percent of independents, and 72 percent of Democrats). In 2012, several surveys of voters attitudes toward growing income inequality found the issue ranked less important than other economic issues such as growth and equality of opportunity, and relatively low in affecting voters "personally". In 1998 a Gallup poll had found 52% of Americans agreeing that the gap between rich and the poor was a problem that needed to be fixed, while 45% regarded it as "an acceptable part of the economic system". In 2011, those numbers are reversed: Only 45% see the gap as in need of fixing, while 52% do not. However, there was a large difference between Democrats and Republicans, with 71% of Democrats calling for a fix.
In contrast, a January 2014 poll found 61% of Republicans, 68% of Democrats and 67% of independents accept the notion that income inequality in the US has been growing over the last decade. The Pew Center poll also indicated that 69% of Americans supported the government doing "a lot" or "some" to address income inequality and that 73% of Americans supported raising the minimum wage from $7.25 to $10.10 per hour.
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Opinion surveys of what respondents thought was the right level of inequality have found Americans no more accepting of income inequality than other citizens of other nations, but more accepting of what they thought the level of inequality was in their country, being under the impression that there was less inequality than there actually was. Dan Ariely and Michael Norton show in a study (2011) that US citizens across the political spectrum significantly underestimate the current US wealth inequality and would prefer a more egalitarian distribution of wealth. Joseph Stiglitz in "The Price of Inequality" has argued that this sense of unfairness has led to distrust in government and business.
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Measuring Poverty Rates The Two Official Measures of Poverty There are two basic versions of the federal poverty measure: the poverty thresholds (which are the primary version) and the poverty guidelines. The Census Bureau issues the poverty thresholds, which are generally used for statistical purposes—for example, to estimate the number of people in poverty nationwide each year and classify them by type of residence, race, and other social, economic, and demographic characteristics. The Department of Health and Human Services issues the poverty guidelines for administrative purposes—for instance, to determine whether a person or family is eligible for assistance through various federal programs. Since the 1960s, the United States government has defined poverty in absolute terms. When the Johnson administration declared "war on poverty" in 1964, it chose an absolute measure. The "absolute poverty line" is the threshold below which families or individuals are considered to be lacking the resources to meet the basic needs for healthy living; having insufficient income to provide the food, shelter and clothing needed to preserve health.
The "Orshansky Poverty Thresholds" form the basis for the current measure of poverty in the U.S. Mollie Orshansky was an economist working for the Social Security Administration (SSA). Her work appeared at an opportune moment. Orshansky's article was published later in the same year that Johnson declared war on poverty. Since her measure was absolute (i.e., did not depend on other events), it made it possible to objectively answer whether the U.S. government was "winning" this war. The newly formed United States Office of Economic Opportunity adopted
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the lower of the Orshansky poverty thresholds for statistical, planning, and budgetary purposes in May 1965. The Bureau of the Budget (now the Office of Management and Budget) adopted Orshansky's definition for statistical use in all Executive departments. The measure gave a range of income cutoffs, or thresholds, adjusted for factors such as family size, sex of the family head, number of children under 18 years old, and farm or non-farm residence. The economy food plan (the least costly of four nutritionally adequate food plans designed by the Department of Agriculture) was at the core of this definition of poverty. At the time of creating the poverty definition, the Department of Agriculture found that families of three or more persons spent about one third of their after-tax income on food. For these families, poverty thresholds were set at three times the cost of the economy food plan.
Different procedures were used for calculating poverty thresholds for two-person households and persons living alone. Annual updates of the SSA poverty thresholds were based on price changes in the economy food plan, but updates do not reflect other changes (food is no longer one-third of the after-tax income). Two changes were made to the poverty definition in 1969. Thresholds for non-farm families were tied to annual changes in the Consumer Price Index rather than changes in the cost of the economy food plan. Farm thresholds were raised from 70 to 85% of the non-farm levels. In 1981, further changes were made to the poverty definition. Separate thresholds for "farm" and "female-householder" families were eliminated. The largest family size category became "nine persons or more." Apart from these changes, the U.S. government's approach to measuring poverty has remained static for the past forty years.
Recent Poverty Rate and Guidelines United States Department of Health and Human Services (HHS) figures for poverty in 2014 Persons in 48 Contiguous States Alaska Hawaii Family Unit and D.C. 1 $11,670 $14,580 $13,420 2 $15,730 $19,660 $18,090 3 $19,790 $24,730 $22,760 4 $23,850 $29,820 $27,430 5 $27,910 $34,900 $32,100 6 $31,970 $39,980 $36,770 7 $36,030 $45,060 $41,440 8 $40,090 $50,140 $46,110 Each additional $4,060 $5,080 $4,670 person adds
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The poverty guideline figures are not the figures the Census Bureau uses to calculate the number of poor persons. The figures that the Census Bureau uses are the poverty thresholds. The Census Bureau provides an explanation of the difference between poverty thresholds and guidelines. The Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. The 2010 figure for a family of 4 with no children under 18 years of age is $22,541, while the figure for a family of 4 with 2 children under 18 is $22,162. For comparison, the 2011 HHS poverty guideline for a family of 4 is $22,350.
Numbers in Other Countries Comparing poverty levels among countries is difficult, because a variety of factors are at play. The official number of poor in the United States in 2008 is about 39.1 million people, greater in number but not percentage than the officially poor in Indonesia, which has a far lower Human Development Index and the next largest population after the United States. While the percentages appear the same, the actual income is not the same among both groups of people living below their nation's poverty levels. Understanding the many aspects not of using but of comparing poverty definitions can aid perception. The Human Poverty Index (HPI), or Human Development Index (HDI), may help global comparison in quality of living, but comparisons must be adjusted for differences within countries.
Relative Measures of Poverty Another way of looking at poverty is in relative terms. "Relative poverty" can be defined as having significantly less income and wealth than other members of society. Therefore, the relative poverty rate is a measure of income inequality. When the standard of living among those in more financially advantageous positions rises while that of those considered poor stagnates, the relative poverty rate will reflect such growing income inequality and increase. Conversely, the relative poverty rate can decrease, with low income people coming to have less wealth and income if wealthier people's wealth is reduced by a larger percentage than theirs. In 1959, a family at the poverty line had an income that was 42.64% of the median income. If the poverty line in 1999 was less than 42.64% of the median income, then relative poverty would have increased. Some critics argue that relying on income disparity to determine who is impoverished can be misleading. The Bureau of Labor Statistics data suggests that consumer spending varies much less than income. In 2008, the "poorest" one fifth of Americans households spent on average $12,955 per person for goods and services (other than taxes), the second quintile spent $14,168, the third $16,255, the fourth $19,695, while the "richest" fifth spent $26,644. The disparity of expenditures is much less than the disparity of income.
Income Distribution and Relative Poverty Although the relative approach theoretically differs largely from the Orshansky definition, crucial variables of both poverty definitions are more similar than often thought. First, the soPage 18 of 102
called standardization of income in both approaches is very similar. To make incomes comparable among households of different sizes, equivalence scales are used to standardize household income to the level of a single person household. When compared to the US Census poverty line, which is based on a defined basket of goods, for the most prevalent household types both standardization methods show very similar results.
Measurement Approaches
Overview Various methods are used to determine income inequality and different sources may give different figures for gini coefficients or ratio different ratio of percentiles, etc.. The United States Census Bureau studies on inequality of household income and individual income show lower levels of inequality than some other sources (Saez and Piketty, and the CBO), but do not include data for the highest-income households where most of change in income distribution has occurred. Two commonly cited sources of income inequality data are the CBO and economist Emmanuel Saez, which differ somewhat in their sources and methods. According to Saez, for 2011 the share of "market income less transfers" received by the top 1% was about 19.5%. Saez used IRS data in this measure. The CBO uses both IRS data and Census data in its computations and reported a lower "pre-tax" figure for the top 1% of 14.6%. The two data series were approximately 5 percentage points apart in recent years.
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Internal Revenue Service (IRS) data Pioneers in the use of IRS income data to analyze income distribution are Emmanuel Saez and Thomas Piketty at the Paris School of Economics showed that the share of income held by the top 1 percent was as large in 2005 as in 1928. Other sources that have noted the increased inequality included economist Janet Yellen who stated, "the growth [in real income] was heavily concentrated at the very tip of the top, that is, the top 1 percent." Follow-up research, published in 2014, by Emmanuel Saez and Gabriel Zucman revealed that more than half of those in the top 1 percent had not experienced relative gains in wealth between 1960 and 2012. In fact, those between the top 1 percent and top .5 percent had actually lost relative wealth. Only those in the top .1 percent and above had made relative wealth gains during that time.
Census Bureau Data The comparative use of Census Bureau data, as well as most sources of demographic income data, has been questioned by statisticians for being unable to account for 'mobility of incomes'. At any given time, the Census Bureau ranks all households by household income and then divides this distribution of households into quintiles. The highest-ranked household in each quintile provides the upper income limit for each quintile. Comparing changes in these upper income limits for different quintiles is how changes are measured between one moment in time and the next. The problem with inferring income inequality on this basis is that the census statistics provide only a snapshot of income distribution in the U.S., at individual points in time. The statistics do not reflect the reality that income for many households changes over time—i.e., incomes are mobile. For most people, income increases over time as they move from their first, low-paying job in high school to a better-paying job later in their lives. Also, some people lose income over time because of business-cycle contractions, demotions, career changes, retirement, etc. The implication of changing individual incomes is that individual households do not remain in the same income quintiles over time. Thus, comparing different income quintiles over time is like comparing apples to oranges, because it means comparing incomes of different people at different stages in their earnings profile. Gary Burtless of the Brookings Institution notes that many economists and analysts who use U.S. census data fail to recognize recent and significant lower- and middle-income gains, primarily because census data does not capture key information: "A commonly used indicator of middle class income is the Census Bureau's estimate of median household money income. The main problem with this income measure is that it only reflects households' before-tax cash incomes. It fails to account for changing tax burdens and the impact of income sources that do not take the form of cash. This means, for example, that tax cuts in 2001-2003 and 2008-2012 are missed in the census statistics. Furthermore, the Census Bureau measure ignores income received as inkind benefits and health insurance coverage from employers and the government. By ignoring such benefits as well as sizeable tax cuts in the recession, the Census Bureau's money income measure seriously overstated the income losses that middle-income families suffered in the recession. New CBO income statistics are beginning to show the growing importance of these items. In 1980, in-kind benefits and employer and government spending on health insurance accounted for Page 20 of 102
just 6% of the after-tax incomes of households in the middle one-fifth of the distribution. By 2010 these in-kind income sources represented 17% of middle class households' after-tax income. The income items missed by the Census Bureau are increasing faster than the income items included in its money income measure. What many observers miss, however, is the success of the nation's tax and transfer systems in protecting low- and middle-income Americans against the full effects of a depressed economy. As a result of these programs, the spendable incomes of poor and middle-class families have been better insulated against recessiondriven losses than the incomes of Americans in the top 1%. As the CBO statistics demonstrate, incomes in the middle and at the bottom of the distribution have fared better since 2000 than incomes at the very top."
Income Measures: Pre-and Post-Tax Inequality can be measured before and after the effects of taxes and transfer payments such as social security and unemployment insurance.
Market income, or income before taxes & transfers: Expertise, productiveness and work experience, inheritance, gender, and race have had a strong influence on distribution of personal income in the United States as in other countries. After taxes & transfers: Reducing the progressivity of the income tax system and transfers increases income inequality. CBO reported in 2011 that: "The equalizing effect of transfers declined over the 1979–2007 period primarily because the distribution of transfers became less progressive. The equalizing effect of federal taxes also declined over the period, in part because the amount of federal taxes shrank as a share of market income and in part because of changes in the progressivity of the federal tax system."
Demographic Issues Comparisons of income over time should adjust for changes in average age, family size, number of breadwinners, and other characteristics of a population. Measuring personal income ignores dependent children, but household income also has problems – a household of ten has a lower standard of living than one of two people, though the income of the two households may be the same. People's earnings tend to rise over their working lifetimes, so "snapshot measures of income inequality can be misleading." The inequality of a recent college graduate and a 55-yearold at the peak of his/her career is not an issue if the graduate has the same career path.
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Conservative researchers and organizations have focused on the flaws of household income as a measure for standard of living in order to refute claims that income inequality is growing, becoming excessive or posing a problem for society. According to sociologist Dennis Gilbert, growing inequality can be explained in part by growing participation of women in the workforce. High earning households are more likely to be dual earner households, And according to a 2004 analysis of income quintile data by the Heritage Foundation, inequality becomes less when household income is adjusted for size of household. Aggregate share of income held by the upper quintile (the top earning 20 percent) decreases by 20.3% when figures are adjusted to reflect household size. However the Pew Research Center found household income has appeared to decline less than individual income in the twenty-first century because those who are no longer able to afford their own housing have increasingly been moving in with relatives, creating larger households with more income earners in them. The 2011 CBO study "Trends in the Distribution of Household Income" mentioned in this article adjusts for household size so that its quintiles contain an equal number of people, not an equal number of households. Looking at the issue of how frequently workers or households move into higher or lower quintiles as their income rises or falls over the years, the CBO found income distribution over a multi-year period "modestly" more equal than annual income. The CBO study confirms earlier studies. Overall, according to Timothy Noah, correcting for demographic factors (today's population is older than it was 33 years ago, and divorce and single parenthood have made households smaller), you find that income inequality, though less extreme than shown by the standard measure, is also growing faster than shown by the standard measure.
Gini index The Gini coefficient summarizes income inequality in a single number and is one of the most commonly used measures of income inequality. It uses a scale from 0 to 1 – the higher the number the more inequality. Zero represents perfect equality (everyone having exactly the same income), and 1 represents perfect inequality (one person having all income). (Index scores are commonly multiplied by 100 to make them easier to understand.) Gini index ratings can be used to compare inequality within (by race, gender, employment) and between countries, before and after taxes. Different sources will often give different gini values for the same country or population measured. For example, the U.S. Census Bureau's official Gini coefficient for the United States was 47.6 in 2013, up from 45.4 in 1993, the earliest year for comparable data. By contrast, the OECD's Gini coefficient for income inequality in the United States is 37 in 2012 (including wages and other cash transfers), which is still the highest in the developed world, with the lowest being Denmark (24.3), Norway (25.6), and Sweden (25.9). Professor Salvatore Babones of the University of Sydney notes: A major gap in the measurement of income inequality is the exclusion of capital gains, profits made on increases in the value of investments. Capital gains are excluded for purely practical reasons. The Census doesn‘t ask about them, so they can‘t be included in inequality statistics.
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Obviously, the rich earn much more from investments than the poor. As a result, real levels of income inequality in America are much higher than the official Census Bureau figures would suggest.
Measuring Inequality through Consumption vs. Income Conservative researchers have argued that income inequality is not significant because consumption, rather than income should be the measure of inequality, and inequality of consumption is less extreme than inequality of income in the US. Will Wilkinson of the libertarian Cato Institute states that "the weight of the evidence shows that the run-up in consumption inequality has been considerably less dramatic than the rise in income inequality," and consumption is more important than income. According to Johnson, Smeeding, and Tory, consumption inequality was actually lower in 2001 than it was in 1986. The debate is summarized in "The Hidden Prosperity of the Poor" by journalist Thomas B. Edsall. Other studies have not found consumption inequality less dramatic than household income inequality, and the CBO's study found consumption data not "adequately" capturing "consumption by highincome households" as it does their income, though it did agree that household consumption numbers show more equal distribution than household income. Others dispute the importance of consumption over income, pointing out that if middle and lower income are consuming more than they earn it is because they are saving less or going deeper into debt. A "growing body of work" suggests that income inequality has been the driving factor in the growing household debt, as high earners bid up the price of real estate and middle income earners go deeper into debt trying to maintain what once was a middle class lifestyle. Between 1983 and 2007, the top 5 percent saw their debt fall from 80 cents for every dollar of income to 65 cents, while the bottom 95 percent saw their debt rise from 60 cents for every dollar of income to $1.40. Economist Krugman has found a strong correlation between inequality and household debt in America over the last hundred years.
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The Demographics of Poverty in the US In addition to family status, race/ethnicity and age also correlate with high poverty rates in the United States. Although data regarding race and poverty are more extensively published and cross tabulated, the family status correlation is by far the strongest.
Poverty and Family Status Homeless children in the United States: The number of homeless children reached record highs in 2011, 2012, and 2013 at about three times their number in 1983. According to the US Census, in 2007 5.8% of all people in married families lived in poverty, as did 26.6% of all persons in single parent households and 19.1% of all persons living alone. More than 75% of all poor households are headed by women (2012).
By Race/Ethnicity and Family Status, Based on Data From 2007 Camden, New Jersey is one of the poorest cities in the United States. Among married couple families: 5.8% lived in poverty. This number varied by race and ethnicity as follows: 5.4% of all white persons (which includes white Hispanics), 9.7% of all black persons (which includes black Hispanics), and 14.9% of all Hispanic persons (of any race) living in poverty. Among single parent (male or female) families: 26.6% lived in poverty. This number varied by race and ethnicity as follows: 22.5% of all white persons (which includes white Hispanics), 44.0% of all black persons (which includes black Hispanics), and 33.4% of all Hispanic persons (of any race) living in poverty. Among individuals living alone: 19.1% lived in poverty. This number varied by race and ethnicity as follows:
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18% of white persons (which includes white Hispanics) 28.9% of black persons (which includes black Hispanics) and 27% of Hispanic persons (of any race) living in poverty.
Poverty and Race/Ethnicity The US Census declared that in 2010 15.1% of the general population lived in poverty: 9.9% of all white persons 12.1% of all Asian persons 26.6% of all Hispanic persons (of any race) 28.4% of all black persons. About half of those living in poverty are non-Hispanic white (19.6 million in 2010),[43] but poverty rates are much higher for blacks and Hispanics. Non-Hispanic white children comprised 57% of all poor rural children. In FY 2009, black families comprised 33.3% of TANF families, non-Hispanic white families comprised 31.2%, and 28.8% were Hispanic.
Poverty Among Native Americans Poverty is also notoriously high on Native American reservations. 7 of the 11 poorest counties in per capita income, including the 2 poorest in the U.S., encompass Lakota Sioux reservations in South Dakota. This fact has been cited by some critics as a mechanism that enables the "kidnapping" of Lakota children by the state of South Dakota's Department of Social Services. The Lakota People's Law Project, among other critics, allege that South Dakota "inappropriately equates economic poverty with neglect...South Dakota's rate of identifying 'neglect' is 18% higher than the national average...In 2010, the national average of state discernment of neglect, as a percent of total maltreatment of foster children prior to their being taken into custody by the state, was 78.3%. In South Dakota the rate was 95.8%." Poverty in the Pine Ridge Reservation in particular has had unprecedented effects on its residents' longevity. "Recent reports state the average life expectancy is 45 years old while others state that it is 48 years old for men and 52 years old for women. With either set of figures, that's the shortest life expectancy for any community in the Western Hemisphere outside Haiti, according to The Wall Street Journal"
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Poverty and Age Poverty rates by gender and work for Americans aged 65 and over The US Census declared that in 2010 15.1% of the general population lived in poverty: 22% of all people under age 18 13.7% of all people 19–64, and 9% of all people ages 65 and older The Organization for Economic Co-operation and Development (OECD) uses a different measure for poverty and declared in 2008 that child poverty in the US is 20% and poverty among the elderly is 23%. The non-profit advocacy group Feeding America has released a study (May 2009) based on 2005–2007 data from the U.S. Census Bureau and the Agriculture Department, which claims that 3.5 million children under the age of 5 are at risk of hunger in the United States. The study claims that in 11 states, Louisiana, which has the highest rate, followed by North Carolina, Ohio, Kentucky, Texas, New Mexico, Kansas, South Carolina, Tennessee, Idaho and Arkansas, more than 20 percent of children under 5 are allegedly at risk of going hungry. (Receiving fewer than 1,800 calories per day) The study was paid by ConAgra Foods, a large food company.
Children in Poverty In 2012, 16.1 million children were living in poverty. Out of the 49 million Americans living in food insecure homes, 15.9 million of them were children. In 2013, child poverty reached record high levels in the U.S., with 16.7 million children living in food insecure households. Many of the neighborhoods these children reside in lack basic produce and nutritious food. 47 million Americans depend on food banks, more than 30% above 2007 levels. Households headed by single mothers are most likely to be affected. 30 percent of low income single mothers can't even afford diapers. Inability to afford this necessity can cause a chain reaction, including mental, health, and behavioral problems. Some women are forced to make use of one or two diapers, using them more than once. This causes rashes and sanitation problems as well as health problems. Without diapers, children are unable to enter into daycare. The lack of childcare can be detrimental to single mothers, hindering their ability to obtain employment. Worst affected are the District of Columbia, Oregon, Arizona, New Mexico and Florida, while North Dakota, New Hampshire, Virginia, Minnesota and Massachusetts are the least affected. 31 million lowincome children received free or reduced-price meals daily through the National School lunch program during the 2012 federal fiscal year. Nearly 14 million children are estimated to be served by Feeding America with over 3 million being of the ages of 5 and under. A 2014 report by the National Center on Family Homelessness states the number of homeless children in the U.S. has reached record levels, calculating that 2.5 million children, or one child in every 30, experienced homelessness in 2013. High levels of poverty, lack of affordable housing and domestic violence were cited as the primary causes. Page 27 of 102
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The Effects of Education Poverty affects individual access to quality education. The U.S. education system is funded by local communities; therefore the quality of materials and teachers reflects the affluence of community. Low income communities are not able to afford the quality education that high income communities do. Another important aspect of education in low income communities is the apathy of both students and teachers. To some, the children of the poor or ignorant are mere copies of their parents fated to live at the same level of income and education as their parents. The effect of such a perception can manifest itself in teachers who will not put forth the effort to teach and students who are opposed to learning; in both cases poor students are thought to be incapable. Females in poverty are also likely to become pregnant at a young age, and with fewer resources to care for a child, young women often drop out of school. Due to these and other reasons the quality of education between the classes is not equal.
Education and Human Capital Disagreeing with this focus on the topearning 1%, and urging attention to the economic and social pathologies of lowerincome/lower education Americans, is conservative journalist David Brooks. Whereas in the 1970s, high school and college graduates had "very similar family structures", today, high school grads are much less likely to get married and be active in their communities, and much more likely to smoke, be obese, get divorced, or have "a child out of wedlock." The zooming wealth of the top one percent is a problem, but it's not nearly as big a problem as the tens of millions of Americans who have dropped out of high school or college. It's not nearly as big a problem as the 40 percent of children who are born out of wedlock. It's not nearly as big Page 30 of 102
a problem as the nation's stagnant human capital, its stagnant social mobility and the disorganized social fabric for the bottom 50 percent. Contradicting most of these arguments, classical liberals such as Friedrich Hayek have maintained that because individuals are diverse and different, state intervention to redistribute income is inevitably arbitrary and incompatible with the concept of general rules of law, and that "what is called 'social' or distributive' justice is indeed meaningless within a spontaneous order". Those who would use the state to redistribute, "take freedom for granted and ignore the preconditions necessary for its survival."
Food Security Eighty-nine percent of the American households were food secure throughout the entire year of 2002, meaning that they had access at all times to enough food for an active, healthy life for all of the household members. The remaining households were food insecure at least some time during that year. The prevalence of food insecurity rose from 10.7% in 2001 to 11.1% in 2002, and the prevalence of food insecurity with hunger rose from 3.3% to 3.5%. In 2007, 88.9% of American households were food secure throughout the entire year. The number of American households that were food secure throughout the entire year dropped to 85.4% in 2008. As of 2012, the prevalence of food insecurity has been essentially unchanged since 2008.
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Risk Factors That Lead to Poverty There are numerous factors related to poverty in the United States. 
According to the American Enterprise Institute, research has shown that income and intelligence are related. In a 1998 study, Charles Murray compared the earnings of 733 full sibling pairs with differing intelligence quotients (IQs). He referred to the sample as utopian in that the sampled pairs were raised in families with virtually no illegitimacy, divorce or poverty. The average earnings of sampled individuals with an IQ of under 75 was $11,000, compared to $16,000 for those with an IQ between 75 and 90, $23,000 for those with an IQ between 90 and 110, $27,000 for those with an IQ between 110 and 125, and $38,000 for those with an IQ above 125. Murray's work on IQ has been criticized by Stephen Jay Gould, LoĂŻc Wacquant and others.
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Income has a high correlation with educational levels. In 2007, the median earnings of
household headed by individuals with less than a 9th grade education was $20,805 while households headed by high school graduates earned $40,456, households headed by holders of bachelor's degrees earned $77,605, and families headed by individuals with professional degrees earned $100,000.
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In many cases poverty is caused by job loss. In 2007, the poverty rate was 21.5% for individuals who were unemployed, but only 2.5% for individuals who were employed full-time.
In 1991, 8.3% of children in two-parent families were likely to live in poverty; 19.6% of children lived with a father in a single parent family; and 47.1% in a single parent family headed by a mother.
Income levels vary with age. For example, the median 2009 income for households headed by individuals age 15–24 was only $30,750, but increased to $50,188 for household headed by individuals age 25–34 and $61,083 for household headed by individuals 35–44. Work experience and additional education may be factors.
Income levels vary along racial/ethnic lines: 21% of all children in the United States live in poverty, about 46% of black children and 40% of Latino children. The poverty rate is 9.9% for black married couples, and only 30% of black children are born to married couples (see Marriage below). The poverty rate for native born and naturalized whites is identical (9.6%). On the other hand, the poverty rate for naturalized blacks is 11.8% compared to 25.1% for native born blacks, suggesting race alone does not explain income disparity. Not all minorities have low incomes. Asian families have higher incomes than all other ethnic groups. For example, the 2005 median income of Asian families was $68,957 compared to the median income of white families of $59,124. Asians, however, report discrimination occurrences more frequently than blacks. Specifically, 31% of Asians reported employment discrimination compared to 26% of blacks in 2005.
The relationship between tax rates and poverty is disputed. A study comparing high tax Scandinavian countries with the U. S. suggests high tax rates are inversely correlated with poverty rates. The poverty rate, however, is low in some low tax countries like Switzerland. A comparison of poverty rates between states reveals that some low tax states have low poverty rates. For example, New Hampshire has the lowest poverty rate of any state in the U. S., and has very low taxes (46th among all states). It is true however that both Switzerland and New Hampshire have a very high household income and other measures offsetting the lack of taxation. For example, Switzerland has Universal Healthcare and a free system of education for children as young as four years old. New Hampshire has no state income tax or sales tax, but does have the nation's highest property taxes.
The Heritage Foundation speculates that illegal immigration increases job competition among low wage earners, both native and foreign born. Additionally many first generation immigrants, namely those without a high school diploma, are also living in poverty themselves.
Economist Jared Bernstein and Elise Gould of the Economic Policy Institute suggest that poverty could have decreased significantly if inequality had not increased over the last few decades.
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The poor in the United States are incarcerated at a much higher rate than their counterparts in other developed nations, with penal confinement being, according to sociologist Bruce Western, "commonplace for poor men of working age." A 2015 study by the Vera Institute of Justice contends that jails in the U.S. have become "massive warehouses" of the impoverished since the 1980s. Writing in The Routledge Handbook of Poverty in the United States, the scholars Reuben Jonathan Miller and Emily Shayman state that the shift to neoliberal policies "has more deeply embedded the carceral state within the lives of the poor, transforming what it means to be poor in America."
Concerns Regarding Accuracy In recent years, there have been a number of concerns raised about the official U.S. poverty measure. In 1995, the National Research Council's Committee on National Statistics convened a panel on measuring poverty. The findings of the panel were that "the official poverty measure in the United States is flawed and does not adequately inform policy-makers or the public about who is poor and who is not poor." The panel was chaired by Robert Michael, former Dean of the Harris School of the University of Chicago. According to Michael, the official U.S. poverty measure "has not kept pace with farreaching changes in society and the economy." The panel proposed a model based on disposable income:
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According to the panel's recommended measure, income would include, in addition to money received, the value of non-cash benefits such as food stamps, school lunches and public housing that can be used to satisfy basic needs. The new measure also would subtract from gross income certain expenses that cannot be used for these basic needs, such as income taxes, child-support payments, medical costs, health-insurance premiums and work-related expenses, including child care.
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Understating Poverty Many sociologists and government officials have argued that poverty in the United States is understated, meaning that there are more households living in actual poverty than there are households below the poverty threshold. A recent NPR report states that as many as 30% of Americans have trouble making ends meet and other advocates have made supporting claims that the rate of actual poverty in the US is far higher than that calculated by using the poverty threshold. A study taken in 2012 estimated that roughly 38% of Americans live "paycheck to paycheck." According to William H. Chafe, if one used a relative standard for measuring poverty (a standard that took into account the rising standards of living rather than an absolute dollar figure) then 18% of families were living in poverty in 1968, not 13% as officially estimated at that time.
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As far back as 1969, the Bureau of Labor Statistics put forward suggested budgets for adequate family living. 60% of working-class Americans lived below one of these budgets, which suggested that a far higher proportion of Americans lived in poverty than the official poverty line suggested. These findings were also used by observers on the left when questioning the longestablished view that most Americans had attained an affluent standard of living in the two decades following the end of the Second World War. A neighborhood of poor white southerners, Chicago, 1974 Using a definition of relative poverty (reflecting disposable income below half the median of adjusted national income), it was estimated that, between 1979 and 1982, 17.1% of Americans lived in poverty. As noted above, the poverty thresholds used by the US government were originally developed during the Johnson administration's War on Poverty initiative in 1963– 1964. Mollie Orshansky, the government economist working at the Social Security Administration who developed the thresholds, based the threshold levels on the cost of purchasing what in the mid-1950s had been determined by the US Department of Agriculture to be the minimal nutritionally-adequate amount of food necessary to feed a family. Orshansky multiplied the cost of the food basket by a factor of three, under the assumption that the average family spent one third of its income on food. While the poverty threshold is updated for inflation every year, the basket of food used to determine what constitutes being deprived of a socially acceptable minimum standard of living has not been updated since 1955. As a result, the current poverty line only takes into account food purchases that were common more than 50 years ago, updating their cost using the Consumer Price Index. When methods similar to Orshansky's were used to update the food basket using prices for the year 2000 instead of from nearly a half century earlier, it was found that the poverty line should actually be 200% higher than the official level being used by the government in that year.
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Yet even that higher level could still be considered flawed, as it would be based almost entirely on food costs and on the assumption that families still spend a third of their income on food. In fact, Americans typically spent less than one tenth of their after-tax income on food in 2000. For many families, the costs of housing, health insurance and medical care, transportation, and access to basic telecommunications take a much larger bite out of the family's income today than a half century ago; yet, as noted above, none of these costs are considered in determining the official poverty thresholds. According to John Schwarz, a political scientist at the University of Arizona:
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The official poverty line today is essentially what it takes in today's dollars, adjusted for inflation, to purchase the same poverty-line level of living that was appropriate to a half century ago, in 1955, for that year furnished the basic data for the formula for the very first poverty measure. Updated thereafter only for inflation, the poverty line lost all connection over time with current consumption patterns of the average family. Quite a few families then didn't have their own private telephone, or a car, or even a mixer in their kitchen... The official poverty line has thus been allowed to fall substantially below a socially decent minimum, even though its intention was to measure such a minimum.
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The issue of understating poverty is especially pressing in states with both a high cost of living and a high poverty rate such as California where the median home price in May 2006 was determined to be $564,430. With half of all homes being priced above the half million dollar mark and prices in urban areas such as San Francisco, San Jose or Los Angeles being higher than the state average, it is almost impossible for not just the poor but also lower middle class worker to afford decent housing, and there is no possibility of home ownership. In the Monterey area, where the low-pay industry of agriculture is the largest sector in the economy and the majority of the population lacks a college education, the median home price was determined to be $723,790, requiring an upper middle class income only earned by roughly 20% of all households in the county. Such fluctuations in local markets are, however, not considered in the Federal poverty threshold and thus leave many who live in poverty-like conditions out of the total number of households classified as poor. In 2011, the Census Bureau introduced a new supplementary poverty measure aimed at providing a more accurate picture of the true extent of poverty in the United States. According to this new measure, 16% of Americans lived in poverty in 2011, compared with the official figure of 15.2%. The new measure also estimated that nearly half of all Americans lived in poverty that year, defined as living within 200% of the federal poverty line. Duke University Professor of Public Policy and Economics Sandy Darity, Jr. says, "There is no exact way of measuring poverty. The measures are contingent on how we conceive of and define poverty. Efforts to develop more refined measures have been dominated by researchers who intentionally want to provide estimates that reduce the magnitude of poverty."
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Overstating Poverty Youth play in Chicago's Stateway Gardens high-rise housing project in 1973. Some critics assert that the official U.S. poverty definition is inconsistent with how it is defined by its own citizens and the rest of the world, because the U.S. government considers many citizens statistically impoverished despite their ability to sufficiently meet their basic needs. According to a heavily criticised 2011 paper by The Heritage Foundation research fellow Robert Rector, of the 43.6 million Americans deemed by the U.S. Census Bureau to be below the poverty level in 2009, the majority had adequate shelter, food, clothing and medical care. In addition, the paper stated that those assessed as below the poverty line in 2011 have a much higher quality of living than those who were identified by the census 40 years ago as being in poverty. According to The Heritage Foundation, the federal poverty line also excludes income other than cash income, especially welfare benefits. Thus, if food stamps and public housing were successfully raising the standard of living for poverty stricken individuals, then the poverty line figures would not shift, since they do not consider the income equivalents of such entitlements. A 1993 study of low income single mothers titled Making Ends Meet, by Kathryn Edin, a sociologist at the University of Pennsylvania, showed that the mothers spent more than their reported incomes because they could not "make ends meet" without such expenditures. According to Edin, they made up the difference through contributions from family members, absent boyfriends, off-the-book jobs, and church charity. According to Edin: "No one avoided the unnecessary expenditures, such as the occasional trip to the Dairy Queen, or a pair of stylish new sneakers for the son who might otherwise sell drugs to get them some money or something, or the Cable TV subscription for the kids home alone and you are afraid they will be out on the street if they are not watching TV." However many mothers skipped meals or did odd jobs to cover those expenses. According to Edin, for "most welfare-reliant mothers food and shelter alone cost almost as much as these mothers received from the government. For more than one-third, food and housing costs exceeded their cash benefits, leaving no extra money for uncovered medical care, clothing, and other household expenses."
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Protective Factors That Alleviate Poverty In the age of inequality, such anti-poverty policies are more important than ever, as higher inequality creates both more poverty along with steeper barriers to getting ahead, whether through the lack of early education, nutrition, adequate housing, and a host of other poverty-related conditions that dampen ones chances in life. -
Jared Bernstein
There have been many governmental and nongovernmental efforts to reduce poverty and its effects. These range in scope from neighborhood efforts to campaigns with a national focus. They target specific groups affected by poverty such as children, people who are autistic, immigrants, or people who are homeless. Efforts to alleviate poverty use a disparate set of methods, such as advocacy, education, social work, legislation, direct service or charity, and community organizing. Recent debates have centered on the need for policies that focus on both "income poverty" and "asset poverty." Advocates for the approach argue that traditional governmental poverty policies focus solely on supplementing the income of the poor through programs such as Aid to Families with Dependent Children (AFDC) and Food Stamps.
According to the CFED 2012 Assets & Opportunity Scorecard, 27 percent of households – nearly double the percentage that are income poor – are living in "asset poverty." These families do not have the savings or other assets to cover basic expenses (equivalent to what could be purchased with a poverty level income) for three months if a layoff or other emergency leads to loss of income.
Since 2009, the number of asset poor families has increased by 21 percent from about one in five families to one in four families. In order to provide assistance to such asset poor families, Congress appropriated $24 million to administer the Assets for Independence Program under the supervision of the US Department for Health and Human Services. Page 40 of 102
The program enables community-based nonprofits and government agencies to implement Individual Development Account or IDA programs, which are an asset-based development initiative. Every dollar accumulated in IDA savings is matched by federal and non-federal funds to enable households to add to their assets portfolio by buying their first home, acquiring a postsecondary education, or starting or expanding a small business.
Additionally, the Earned Income Tax Credit (EITC or EIC) is a credit for people who earn lowto-moderate incomes. This credit allows them to get money from the government if their total tax outlay is less than the total credit earned, meaning it is not just a reduction in total tax paid but can also bring new income to the household. The Earned Income Tax Credit is viewed as the largest poverty reduction program in the United States. There is an ongoing debate in the U.S. about what the most effective way to fight poverty is, through the tax code with the EITC, or through the minimum wage laws. Government safety net programs put in place since the War on Poverty have helped reduce the poverty rate from 26% in 1967 to 16% in 2012, according to a Supplemental Poverty Model(SPM) created by Columbia University, while the official U.S. Poverty Rate has not changed, as the economy by itself has done little to reduce poverty. According to the 2013 Columbia University study which created the (SPM) method of measuring poverty, without such programs the poverty rate would be 29% today. An analysis of the study by Kevin Drum suggests the American welfare state effectively reduces poverty among the elderly but provides relatively little assistance to the working-age poor. A 2014 study by Pew Charitable Trusts shows that without social programs like food stamps, social security and the federal EITC, the poverty rate in the U.S. would be much higher. Nevertheless, the U.S. has the weakest social safety net of all developed nations. Sociologist Monica Prasad of Northwestern University argues that this developed because of government intervention rather than lack of it, which pushed consumer credit for meeting citizens' needs rather than applying social welfare policies as in Europe.
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Income Inequality Income inequality in the United States has increased significantly since the 1970s after several decades of stability, meaning the share of the nation's income received by higher income households has increased. This trend is evident with income measured both before taxes (market income) as well as after taxes and transfer payments. Income inequality has fluctuated considerably since measurements began around 1915, moving in an arc between peaks in the 1920s and 2000s, with a 30-year period of relatively lower inequality between 1950-1980.
Measured for all households, U.S. income inequality is comparable to other developed countries before taxes and transfers, but is among the worst after taxes and transfers, meaning the U.S. shifts relatively less income from higher income households to lower income households. Measured for working-age households, market income inequality is comparatively high (rather than moderate) and the level of redistribution is moderate (not low). These comparisons indicate Americans shift from reliance on market income to reliance on income transfers later in life and less fully than do households in other developed countries. The U.S. ranks around the 30th percentile globally, meaning 70% of countries have a more equal income distribution. U.S. federal tax and transfer policies are progressive and therefore reduce income inequality measured after taxes and transfers. Tax and transfer policies together reduced income inequality slightly more in 2011 than in 1979.
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While there is strong evidence that income inequality has increased since the 1970s, there is active debate regarding appropriate measurement, causes, effects and solutions. The two major political parties have different approaches to the issue, with Democrats historically emphasizing that economic growth should result in shared prosperity (i.e., a pro-labor argument advocating income redistribution), while Republicans tend to downplay the validity, relevance or ability to positively influence the issue (i.e., a pro-capital argument against redistribution).
Overview U.S. pre-tax and after-tax income share of top 1% households from 1979-2011, for commonly cited data series (CBO and Piketty-Saez) U.S. income inequality has grown significantly since the early 1970s,[8][9][10][11][12][13] after several decades of stability, and has been the subject of study of many scholars and institutions. The U.S. consistently exhibits higher rates of income inequality than most developed nations due to the nation's enhanced support of free market capitalism and less progressive spending on social services. The top 1% of income earners received approximately 20% of the pre-tax income in 2013, versus approximately 10% from 1950 to 1980. The top 1% is not homogeneous, with the very top income households pulling away from others in the top 1%. For example, the top 0.1% of households received approximately 10% of the pre-tax income in 2013, versus approximately 34% between 1951-1981. Most of the growth in income inequality has been between the middle class and top earners, with the disparity widening the further one goes up in the income distribution. To put this change into perspective, if the US had the same income distribution it had in 1979, each family in the bottom 80% of the income distribution would have $11,000 more per year in income on average, or $916 per month. Half of the U.S. population lives in poverty or is lowincome, according to U.S. Census data. The trend of rising income inequality is also apparent after taxes and transfers. A 2011 study by the CBO found that the top earning 1 percent of households increased their income by about 275% after federal taxes and income transfers over a period between 1979 and 2007, compared to a gain of just under 40% for the 60 percent in the middle of America's income distribution. U.S. federal tax and transfer policies are progressive and therefore substantially reduce income inequality measured after taxes and transfers. They became moderately less progressive between
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1979 and 2007 but slightly more progressive measured between 1979 and 2011. Income transfers had a greater impact on reducing inequality than taxes from 1979 to 2011. Americans are not generally aware of the extent of inequality or recent trends. There is a direct relationship between actual income inequality and the public's views about the need to address the issue in most developed countries, but not in the U.S., where income inequality is worse but the concern is lower. The U.S. was ranked the 41st worst among 141 countries (30th percentile) on income equality measured by the Gini index.
There is significant and ongoing debate as to the causes, economic effects, and solutions regarding income inequality. While before-tax income inequality is subject to market factors (e.g., globalization, trade policy, labor policy, and international competition), after-tax income inequality can be directly affected by tax and transfer policy. U.S. income inequality is comparable to other developed nations before taxes and transfers, but is among the worst after taxes and transfers. Income inequality may contribute to slower economic growth, reduced income mobility, higher levels of household debt, and greater risk of financial crises and deflation. Labor (workers) and capital (owners) have always battled over the share of the economic pie each obtains. The influence of the labor movement has waned in the U.S. since the 1960s along with union participation and more pro-capital laws. The share of total worker compensation has declined from 58% of national income (GDP) in 1970 to nearly 53% in 2013, contributing to income inequality. This has led to concerns that the economy has shifted too far in favor of capital, via a form of corporatism, corpocracy or neoliberalism.
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Although some have spoken out in favor of moderate inequality as a form of incentive, others have warned against the current high levels of inequality, including Yale Nobel prize for economics winner Robert J. Shiller, (who called rising economic inequality "the most important problem that we are facing now today"), former Federal Reserve Board chairman Alan Greenspan, ("This is not the type of thing which a democratic society – a capitalist democratic society – can really accept without addressing"), and President Barack Obama (who referred to the widening income gap as the "defining challenge of our time").
History U.S. income shares of the top 1% and top 0.1% of households 19132013, including capital gains. The level of concentration of income in America has fluctuated throughout its history. Going back to the early 20th Century, when income statistics started to become available, there has been a "great economic arc" from high inequality "to relative equality and back again," in the words of Nobel laureate economist Paul Krugman. In 1915, an era in which the Rockefellers and Carnegies dominated American industry, the richest 1% of Americans earned roughly 18% of all income. By 2007, the top 1 percent account for 24% of all income. In between, their share fell below 10% for three decades. The first era of inequality lasted roughly from the post-civil war era ("the Gilded Age") to sometime around 1937. But from about 1937 to 1947 – a period that has been dubbed the "Great Compression" – income inequality in America fell dramatically. Highly progressive New Deal taxation, the strengthening of unions, and regulation of the National War Labor Board during World War II raised the income of the poor and working class and lowered that of top earners. This "middle class society" of relatively low level of inequality remained fairly steady for about three decades ending in early 1970s, the product of relatively high wages for the US working class and political support for income leveling government policies. Wages remained relatively high because of lack of foreign competition for American manufacturing, lack of low skilled immigrant workers, competition for US workers in general, and – arguably most important – strong trade unions. By 1947 more than a third of non-farm workers were union members, and unions both raised average wages for their membership, and Page 46 of 102
indirectly, and to a lesser extent, raised wages for workers in similar occupations not represented by unions. Scholars believe political support for equalizing government policies was provided by high voter turnout from union voting drives, the support of the otherwise conservative South for the New Deal, and prestige that the massive mobilization and victory of World War II had given the government.
Post-1970 Increase Inflation-adjusted percent increase in pretax and after-tax household income between 1979 and 2011, by pre-tax income group. The return to high inequality – or what Krugman and journalist Timothy Noah have referred as the "Great Divergence" – began in the 1970s. Studies have found income grew more unequal almost continuously except during the economic recessions in 1990–91, 2001 (Dot-com bubble), and 2007 sub-prime bust. The Great Divergence differs in some ways from the pre-Depression era inequality. Before 1937 a larger share of top earners income came from capital (interest, dividends, income from rent, capital gains). Post 1970, income of high-income taxpayers comes predominantly from "labor", i.e. employment compensation. Until 2011, the Great Divergence had not been a major political issue in America, though stagnation of middle class income was. In 2009 the Barack Obama administration White House Middle Class Working Families Task Force convened to focus on economic issues specifically affecting middle-income Americans. In 2011, the Occupy movement drew considerable attention to income inequality in the country. CBO reported that for the 1979-2007 period, after-tax income of households in the top 1 percent of earners grew by 275%, compared to 65% for the next 19 percent, just under 40% for the next 60 percent, 18% for the bottom fifth of households. "As a result of that uneven income growth," the report noted, "the share of total after-tax income received by the 1 percent of the population in households with the highest income more than doubled between 1979 and 2007, whereas the share received by low- and middle-income households declined … The share of income received
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by the top 1 percent grew from about 8% in 1979 to over 17% in 2007. The share received by the other 19 percent of households in the highest income quintile (one-fifth of the population as divided by income) was fairly flat over the same period, edging up from 35% to 36%." According to CBO, the major reason for observed rise in unequal distribution of after-tax income was an increase in market income, that is household income before taxes and transfers. Market income for a household is a combination of labor income (such as cash wages, employer-paid benefits, employer-paid payroll taxes), business income (such as income from businesses and farms operated solely by their owners), capital gains (profits realized from the sale of assets, stock options), capital income (such as interest from deposits, dividends, rental income), and other income. Of these, capital gains accounted for 80% of the increase in market income for the households in top 20%, in the 2000–2007 period. Even over 1991–2000 period, according to CBO, capital gains accounted for 45% of the market income for the top 20% households.
Effects of 2007–2009 Recession Just as higher income groups are more likely to enjoy financial gains when economic times are good, they are also likely to suffer more significant income losses during economic downturns and recessions when compared to lower income groups. This is because higher income groups tend to derive relatively more of their income from more volatile sources related to capital income (i.e., business income, capital gains and dividends), as opposed to labor income (wages and salaries). For example, during 2011 the top 1% of income earners derived 37% of their income from labor income, versus 62% for the middle quintile. On the other hand, the top 1% derived 58% of their income from capital as opposed to 4% for the middle quintile. Government transfers represented only 1% of the income of the top 1%, but 25% for the middle quintile; the dollar amounts of these transfers tend to rise in recessions. This effect occurred during the Great Recession of 2007–2009, when total income going to the bottom 99 percent of Americans declined by 11.6%, but fell by 36.3% for the top 1%. Declines were especially steep for capital gains, which fell by 75% in real (inflation-adjusted) terms between 2007 and 2009. Other sources of capital income also fell: interest income by 40% and dividend income by 33%. Wages, the largest source of income, fell by a more modest 6%. The share of pre-tax income received by the top 1% fell from 18.7% in 2007 to 16.0% in 2008 and 13.4% in 2009, while the bottom four quintiles all had their share of pre-tax income increase from 2007-2009. The share of after-tax income received by the top 1% income group fell from 16.7% in 2007 to 11.5% in 2009.
2009–Present However, the distribution of household incomes has become more unequal during the post-2008 economic recovery as the effects of the recession reversed. CBO reported in November 2014 that the share of pre-tax income received by the top 1% had risen from 13.3% in 2009 to 14.6% in 2011. During 2012 alone, incomes of the wealthiest 1 percent rose nearly 20%, whereas the income of the remaining 99 percent rose 1% in comparison.
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According to an article in The New Yorker, by 2012, the share of pre-tax income received by the top 1% had returned to its pre-crisis peak, at around 23% of the pre-tax income. This is based on widely-cited data from economist Emmanuel Saez, which uses "market income" and relies primarily on IRS data. The CBO uses both IRS data and Census data in its computations and reports a lower pre-tax figure for the top 1%. The two series were approximately 5 percentage points apart in 2011 (Saez at about 19.7% versus CBO at 14.6%), which would imply a CBO figure of about 18% in 2012 if that relationship holds, a significant increase versus the 14.6% CBO reported for 2011. The share of after-tax income received by the top 1% rose from 11.5% in 2009 to 12.6% in 2011. Inflation-adjusted pre-tax income for the bottom 90% of American families fell between 2010 and 2013, with the middle income groups dropping the most, about 6% for the 40th-60th percentiles and 7% for the 20th-40th percentiles. Incomes in the top decile rose 2%. The top 1% captured an estimated 95% of the income growth during the 2009-2012 recovery period, with their pre-tax incomes growing 31.4% adjusted for inflation while the pre-tax incomes of the bottom 99% grew 0.4%. By 2012, the top 10% (top decile) had a 50.4% share of the pre-tax income, the highest level since 1917. Tax increases on higher income earners were implemented in 2013 due to the Affordable Care Act and American Taxpayer Relief Act of 2012. CBO estimated that "average federal tax rates under 2013 law would be higher—relative to tax rates in 2011—across the income spectrum. The estimated rates under 2013 law would still be well below the average rates from 1979 through 2011 for the bottom four income quintiles, slightly below the average rate over that period for households in the 81st through 99th percentiles, and well above the average rate over that period for households in the top 1 percent of the income distribution."
Causes The income growth of the average American family closely matched that of economic productivity until some time in the 1970s. While it began to stagnate, productivity has continued to climb. According to the 2014 Global Wage Report by the International Labor Organization, the widening disparity between wages and productivity is evidence that there has been a significant shift of GDP share going from labor to capital, and this trend is playing a significant role in growing inequality. Page 49 of 102
U.S. income inequality is comparable to other developed countries measured before taxes and transfers, but is among the worst after taxes and transfers.
According to the CBO and others, "the precise reasons for the [recent] rapid growth in income at the top are not well understood", but "in all likelihood," an "interaction of multiple factors" was involved. "Researchers have offered several potential rationales." Some of these rationales conflict, some overlap. They include:
the decline of labor unions. A study in the American Sociological Review, as well as other scholarly research, using the broadest methodology, estimates that the decline of unions may account for from one-third to more than one-half of the rise of inequality among men. As unions weakened, the vast majority of the gains from productivity were taken by senior corporate executives, major shareholders and creditors (e.g. major corporate bondholders, banks and other lenders, etc.). As unions have grown weaker, there has been less pressure on employers to increase wages, or on lawmakers to enact labor-friendly or worker-friendly measures. the globalization hypothesis – low skilled American workers have been losing ground in the face of competition from low-wage workers in Asia and other "emerging" economies; skill-biased technological change – the rapid pace of progress in information technology has increased the demand for the highly skilled and educated so that income distribution favored brains rather than brawn; the superstar hypothesis – modern technologies of communication often turn competition into a tournament in which the winner is richly rewarded, while the runners-up get far less than in the past; immigration of less-educated workers – relatively high levels of immigration of low skilled workers since 1965 may have reduced wages for American-born high school dropouts; policy, politics and race – movement conservatives increased their influence over the Republican Party beginning in the 1970s, moving it politically rightward. Combined with the Party's expanded political power (enabled by a shift of southern white Democrats to the Republican Party following the passage of Civil Rights legislation in the 1960s), this resulted in more regressive tax laws, anti-labor policies, and further limited expansion of the welfare state relative to other developed nations (e.g., the unique absence of universal healthcare). Further, variation in income inequality across developed countries indicates policy has a significant influence on inequality; Japan, Sweden and France have income inequality around 1960 levels.
Paul Krugman put several of these factors into context in January 2015: "Competition from emerging-economy exports has surely been a factor depressing wages in wealthier nations, although probably not the dominant force. More important, soaring incomes at the top were achieved, in large part, by squeezing those below: by cutting wages, slashing benefits, crushing unions, and diverting a rising share of national resources to financial wheeling and dealing...Perhaps more important still, the wealthy exert a vastly disproportionate effect on policy. And elite priorities — obsessive concern with budget deficits, with the supposed need to slash social programs — have done a lot to deepen [wage stagnation and income inequality]."
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Effects: Economic U.S. economic growth is not translating into higher median family incomes. Real GDP per capita has increased since the year 2000 while the real median income per household has not, indicating a trend of greater income inequality. Labor's share of GDP has declined 1970 to 2013, measured based on total compensation as well as salaries & wages. This implies capital's share is increasing. While middle-class family incomes have stagnated as income shifts to the top, the costs of important goods and services continue rising, resulting in a "Middle class squeeze."
Overview There is an ongoing debate as to the economic effects of income inequality. For example, Alan B. Krueger, President Obama's Chairman of the Council of Economic Advisors, summarized the conclusions of several research studies in a 2012 speech. In general, as income inequality worsens:
More income shifts to the wealthy, who tend to spend less of each marginal dollar, causing consumption and therefore economic growth to slow; Income mobility falls, meaning the parents' income is more likely to predict their children's income; Middle and lower-income families borrow more money to maintain their consumption, a contributing factor to financial crises; and The wealthy gain more political power, which results in policies that further slow economic growth.
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Among economists and related experts, many believe that America's growing income inequality is "deeply worrying", unjust, a danger to democracy/social stability, or a sign of national decline. Yale professor Robert Shiller, who was among three Americans who won the Nobel prize for economics in 2013, said after receiving the award, "The most important problem that we are facing now today, I think, is rising inequality in the United States and elsewhere in the world." Economist Thomas Piketty, who has spent nearly 20 years studying inequality primarily in the US, warns that "The egalitarian pioneer ideal has faded into oblivion, and the New World may be on the verge of becoming the Old Europe of the twenty-first century's globalized economy." On the other side of the issue are those who have claimed that the increase is not significant, that it doesn't matter because America's economic growth and/or equality of opportunity are what's important, that it is a global phenomenon which would be foolish to try to change through US domestic policy, that it "has many economic benefits and is the result of ... a well-functioning economy", and has or may become an excuse for "class-warfare rhetoric", and may lead to policies that "reduce the well-being of wealthier individuals".
Economic Growth
Views That Income Inequality Slows Economic Growth Economist Alan B. Krueger wrote in 2012: "The rise in inequality in the United States over the last three decades has reached the point that inequality in incomes is causing an unhealthy division in opportunities, and is a threat to our economic growth. Restoring a greater degree of fairness to the U.S. job market would be good for businesses, good for the economy, and good for the country." Krueger wrote that the significant shift in the share of income accruing to the top 1% over the 1979 to 2007 period represented nearly $1.1 trillion in annual income. Since the wealthy tend to save nearly 50% of their marginal income while the remainder of the population saves roughly 10%, other things equal this would reduce annual consumption (the largest component of GDP) by as much as 5%. Krueger wrote that borrowing likely helped many households make up for this shift, which became more difficult in the wake of the 2007-2009 recession. Inequality in land and income ownership is negatively correlated with subsequent economic growth. A strong demand for redistribution will occur in societies where a large section of the Page 52 of 102
population does not have access to the productive resources of the economy. Rational voters must internalize such issues. High unemployment rates have a significant negative effect when interacting with increases in inequality. Increasing inequality harms growth in countries with high levels of urbanization. High and persistent unemployment also has a negative effect on subsequent long-run economic growth. Unemployment may seriously harm growth because it is a waste of resources, because it generates redistributive pressures and distortions, because it depreciates existing human capital and deters its accumulation, because it drives people to poverty, because it results in liquidity constraints that limit labor mobility, and because it erodes individual self-esteem and promotes social dislocation, unrest and conflict. Policies to control unemployment and reduce its inequality-associated effects can strengthen long-run growth. Concern extends even to such supporters (or former supporters) of laissez-faire economics and private sector financiers. Former Federal Reserve Board chairman Alan Greenspan, has stated reference to growing inequality: "This is not the type of thing which a democratic society – a capitalist democratic society – can really accept without addressing." Some economists (David Moss, Paul Krugman, Raghuram Rajan) believe the "Great Divergence" may be connected to the financial crisis of 2008. Money manager William H. Gross, managing director of PIMCO, criticized the shift in distribution of income from labor to capital that underlies some of the growth in inequality as unsustainable, saying: Even conservatives must acknowledge that return on capital investment, and the liquid stocks and bonds that mimic it, are ultimately dependent on returns to labor in the form of jobs and real wage gains. If Main Street is unemployed and undercompensated, capital can only travel so far down Prosperity Road. He concluded: "Investors/policymakers of the world wake up – you're killing the proletariat goose that lays your golden eggs."
A 2011 study by Ostry and Berg of the factors affecting the duration of economic growth in developed and developing countries, found that income equality has a more beneficial impact on steady growth than trade openness, sound political institutions, or foreign investment. Among economists and reports that find inequality harming economic growth are a December 2013 Associated Press survey of three dozen economists', a 2014 report by Standard and Poor's, economists Gar Alperovitz, Robert Reich, Joseph Stiglitz, and Branko Milanovic. A December 2013 Associated Press survey of three dozen economists found that the majority believe that widening income disparity is harming the US economy. They argue that wealthy
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Americans are receiving higher pay, but they spend less per dollar earned than middle class consumers, the majority of the population, whose incomes have largely stagnated. A 2014 report by Standard and Poor's concluded that diverging income inequality has slowed the economic recovery and could contribute to boom-and-bust cycles in the future as more and more Americans take on debt in order to consume. Higher levels of income inequality increase political pressures, discouraging trade, investment, hiring, and social mobility according to the report. Economists Gar Alperovitz and Robert Reich argue that too much concentration of wealth prevents there being sufficient purchasing power to make the rest of the economy function effectively. Joseph Stiglitz argues that concentration of wealth and income leads the politically powerful economic elite seek to protect themselves from redistributive policies by weakening the state, and this leads to less public investments by the state – roads, technology, education, etc. – that are essential for economic growth. According to economist Branko Milanovic, while traditionally economists thought inequality was good for growth, "The view that income inequality harms growth – or that improved equality can help sustain growth – has become more widely held in recent years. The main reason for this shift is the increasing importance of human capital in development. When physical capital mattered most, savings and investments were key. Then it was important to have a large contingent of rich people who could save a greater proportion of their income than the poor and invest it in physical capital. But now that human capital is scarcer than machines, widespread education has become the secret to growth." He continued that "Broadly accessible education" is both difficult to achieve when income distribution is uneven and tends to reduce "income gaps between skilled and unskilled labor."
Views That Income Inequality Does Not Slow Growth In response to the Occupy movement Richard A. Epstein defended inequality in a free market society, maintaining that "taxing the top one percent even more means less wealth and fewer jobs for the rest of us." According to Epstein, "the inequalities in wealth ... pay for themselves by the vast increases in wealth", while "forced transfers of wealth through taxation ... will destroy the pools of wealth that are needed to generate new ventures. Some researchers have found a connection between lowering high marginal tax rates on high income earners (high marginal tax rates on high income being a common measure to fight inequality), and higher rates of employment growth. Economic sociologist Lane Kenworthy has found no correlation between levels of inequality and economic growth among developed countries, among states of the US, or in the US over the years from 1947 to 2005. Jared Bernstein found a nuanced relation he summed up as follows: "In sum, I'd consider the question of the extent to which higher inequality lowers growth to be an open one, worthy of much deeper research". Tim Worstall commented that capitalism would not
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seem to contribute to an inherited-wealth stagnation and consolidation, but instead appears to promotes the opposite, a vigorous, ongoing turnover and creation of new wealth.
Likelihood of Financial Crises Income inequality was cited as one of the causes of the Great Depression by Supreme Court Justice Louis D. Brandeis in 1933. In his dissent in the Louis K. Liggett Co. v. Lee (288 U.S. 517) case, he wrote: "Other writers have shown that, coincident with the growth of these giant corporations, there has occurred a marked concentration of individual wealth; and that the resulting disparity in incomes is a major cause of the existing depression."
Central Banking economist Raghuram Rajan argues that "systematic economic inequalities, within the United States and around the world, have created deep financial 'fault lines' that have made [financial] crises more likely to happen than in the past" – the Financial crisis of 2007–08 being the most recent example. To compensate for stagnating and declining purchasing power, political pressure has developed to extend easier credit to the lower and middle income earners – particularly to buy homes – and easier credit in general to keep unemployment rates low. This has given the American economy a tendency to go "from bubble to bubble" fueled by unsustainable monetary stimulation.
Monopolization of Labor, Consolidation, and Competition Greater income inequality can lead to monopolization of the labor force, resulting in fewer employers requiring fewer workers.[120][121] Remaining employers can consolidate and take advantage of the relative lack of competition, leading to less consumer choice, market abuses, and relatively higher prices. Page 55 of 102
Aggregate Demand and Debt Income inequality lowers aggregate demand, leading to increasingly large segments of formerly middle class consumers unable to afford as many luxury and essential goods and services. This pushes production and overall employment down. Deep debt may lead to bankruptcy and researchers Elizabeth Warren and Amelia Warren Tyagi found a fivefold increase in the number of families filing for bankruptcy between 1980 and 2005. The bankruptcies came not from increased spending "on luxuries", but from an "increased spending on housing, largely driven by competition to get into good school districts." Intensifying inequality may mean a dwindling number of ever more expensive school districts that compel middle class – or would-be middle class – to "buy houses they can't really afford, taking on more mortgage debt than they can safely handle".
Effects: Socio-Economic Mobility Overview The ability to move from one income group into another (income mobility) is a means of measuring economic opportunity. A higher probability of upward income mobility theoretically would help mitigate higher income inequality, as each generation has a better chance of achieving higher income groups. Conservatives and libertarians such as economist Thomas Sowell, and Congressman Paul Ryan (R., Wisc.) argue that more important than the level of equality of results is America's equality of opportunity, especially relative to other developed countries such as western Europe. However, several studies have indicated that higher income inequality corresponds with lower income mobility. In other words, income brackets tend to be increasingly "sticky" as income inequality increases. This is described by a concept called the Great Gatsby curve. In the words of journalist Timothy Noah, "you can't really experience ever-growing income inequality without experiencing a decline in Horatio Alger-style upward mobility because (to use a frequentlyemployed metaphor) it's harder to climb a ladder when the rungs are farther apart."
Over lifetimes The centrist Brookings Institution said in March 2013 that income inequality was increasing and becoming permanent, sharply reducing social mobility in the US. A 2007 study (by Kopczuk, Saez and Song in 2007) found the top population in America "very stable" and that income mobility had "not mitigated the dramatic increase in annual earnings concentration since the 1970s." Economist Paul Krugman, attacks conservatives for resorting to "extraordinary series of attempts at statistical distortion". He argues that while in any given year, some of the people with low incomes will be "workers on temporary layoff, small businessmen taking writeoffs, farmers hit by bad weather" – the rise in their income in succeeding years is not the same 'mobility' as poor
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people rising to middle class or middle income rising to wealth. It's the mobility of "the guy who works in the college bookstore and has a real job by his early thirties." Studies by the Urban Institute and the US Treasury have both found that about half of the families who start in either the top or the bottom quintile of the income distribution are still there after a decade, and that only 3 to 6% rise from bottom to top or fall from top to bottom. On the issue of whether most Americans do not stay put in any one income bracket, Krugman quotes from 2011 CBO distribution of income study Household income measured over a multi-year period is more equally distributed than income measured over one year, although only modestly so. Given the fairly substantial movement of households across income groups over time, it might seem that income measured over a number of years should be significantly more equally distributed than income measured over one year. However, much of the movement of households involves changes in income that are large enough to push households into different income groups but not large enough to greatly affect the overall distribution of income. Multi-year income measures also show the same pattern of increasing inequality over time as is observed in annual measures. In other words, "many people who have incomes greater than $1 million one year fall out of the category the next year – but that's typically because their income fell from, say, $1.05 million to 0.95 million, not because they went back to being middle class."
Between Generations
Several studies have found the ability of children from poor or middle-class families to rise to upper income – known as "upward relative intergenerational mobility" – is lower in the US than in other developed countries – and at least two economists have found lower mobility linked to income inequality. In their Great Gatsby curve,[130] White House Council of Economic Advisers Chairman Alan B. Krueger and labor economist Miles Corak show a negative correlation between inequality and social mobility. Page 57 of 102
The curve plotted "intergenerational income elasticity" – i.e. the likelihood that someone will inherit their parents' relative position of income level – and inequality for a number of countries. Aside from the proverbial distant rungs, the connection between income inequality and low mobility can be explained by the lack of access for un-affluent children to better (more expensive) schools and preparation for schools crucial to finding high-paying jobs; the lack of health care that may lead to obesity and diabetes and limit education and employment. Krueger estimates that "the persistence in the advantages and disadvantages of income passed from parents to the children" will "rise by about a quarter for the next generation as a result of the rise in inequality that the U.S. has seen in the last 25 years."
Poverty Greater income inequality can increase the poverty rate, as more income shifts away from lower income brackets to upper income brackets. Jared Bernstein wrote: "If less of the economy's market-generated growth – i.e., before taxes and transfers kick in – ends up in the lower reaches of the income scale, either there will be more poverty for any given level of GDP growth, or there will have to be a lot more transfers to offset inequality's poverty-inducing impact." The Economic Policy Institute estimated that greater income inequality would have added 5.5% to the poverty rate between 1979 and 2007, other factors equal. Income inequality was the largest driver of the change in the poverty rate, with economic growth, family structure, education and race other important factors. An estimated 16% of Americans lived in poverty in 2012, versus 26% in 1967.
Further Enrichment of Corporate Top Executives Lisa Shalett, chief investment officer at Merrill Lynch Wealth Management noted that, "for the last two decades and especially in the current period, ... productivity soared ... [but] U.S. real average hourly earnings are essentially flat to down, with today's inflation-adjusted wage equating to about the same level as that attained by workers in 1970. ... So where have the
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benefits of technology-driven productivity cycle gone? Almost exclusively to corporations and their very top executives." Economist Timothy Smeeding summed up the current trend: Americans have the highest income inequality in the rich world and over the past 20–30 years Americans have also experienced the greatest increase in income inequality among rich nations. The more detailed the data we can use to observe this change, the more skewed the change appears to be ... the majority of large gains are indeed at the top of the distribution. According to Janet L. Yellen, chair of the Federal Reserve, ...from 1973 to 2005, real hourly wages of those in the 90th percentile – where most people have college or advanced degrees – rose by 30% or more... among this top 10 percent, the growth was heavily concentrated at the very tip of the top, that is, the top 1 percent. This includes the people who earn the very highest salaries in the U.S. economy, like sports and entertainment stars, investment bankers and venture capitalists, corporate attorneys, and CEOs. In contrast, at the 50th percentile and below – where many people have at most a high school diploma – real wages rose by only 5 to 10%
Policy Responses Overview Economists have proposed a variety of solutions for addressing income inequality. For example, Federal Reserve Chair Janet Yellen described four "building blocks" that could help address income and wealth inequality in an October 2014 speech. These included expanding resources available to children, affordable higher education, business ownership, and inheritance. While before-tax income inequality is subject to market factors, after-tax income inequality can be directly affected by tax and transfer policy. U.S. income inequality is comparable to other developed nations before taxes and transfers, but is among the worst after taxes and transfers. This suggests that more progressive tax and transfer policies would be required to align the U.S. with other developed nations. The Center for American Progress recommended a series of steps in September 2014, including tax reform, subsidizing and reducing healthcare and higher education costs, and strengthening labor influence. However, there is debate regarding whether a public policy response is appropriate for income inequality. For example, Federal Reserve Economist Thomas Garrett wrote in 2010: "It is important to understand that income inequality is a byproduct of a well-functioning capitalist economy. Individuals' earnings are directly related to their productivity...A wary eye should be cast on policies that aim to shrink the income distribution by redistributing income from the more productive to the less productive simply for the sake of 'fairness.'" Public policy responses addressing causes and effects of income inequality include: progressive tax incidence adjustments, strengthening social safety net provisions such as Temporary Assistance for Needy Families, welfare, the food stamp program, Social Security, Medicare, and Page 59 of 102
Medicaid, increasing and reforming higher education subsidies, increasing infrastructure spending, and placing limits on and taxing rent-seeking. Democrat and Republican politicians also provided a series of recommendations for increasing median wages in December 2014. These included raising the minimum wage, infrastructure stimulus, and tax reform.
Resources Available to Children Research shows that children from lower-income households who get good-quality preKindergarten education are more likely to graduate from high school, attend college, hold a job and have higher earnings. In 2010, the U.S. ranked 28th out of 38 advanced countries in the share of four-year-olds enrolled in public or private early childhood education. Gains in enrollment stalled after 2010, as did growth in funding, due to budget cuts arising from the Great Recession. Per-pupil spending in state-funded programs declined by 12% after inflation since 2010. The U.S. differs from other countries in that it funds public education primarily through sub-national (state and local) taxes. The quality of funding for public education varies based on the tax base of the school system, with significant variation in local taxes and spending per pupil. Better teachers also raise the educational attainment and future earnings of students, but they tend to migrate to higher income school districts. Among developed countries, 70% percent of 3year-olds go to preschool, versus 38% in the United States.
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Affordable Higher Education Median annual earnings of full-time workers with a four-year bachelor's degree is 79% higher than the median for those with only a high school diploma. The wage premium for a graduate degree is considerably higher than the undergraduate degree. College costs have risen much faster than income, resulting in an increase in student loan debt from $260 billion in 2004 to $1.1 trillion in 2014. From 1995 to 2013, outstanding education debt grew from 26% of average yearly income to 58%, for households with net worth below the 50th percentile. The unemployment rate is also considerably lower for those with higher educational attainment. A college education is nearly free in many European countries, often funded by higher taxes.
Public Spending and Welfare State Spending The OECD asserts that public spending is vital in reducing the ever expanding wealth gap. Lane Kenworthy advocates incremental reforms to the U.S. welfare state in the direction of the Nordic social democratic model, thereby increasing economic security and equal opportunity. Currently, the U.S. has the weakest social safety net of all developed nations. Welfare spending may entice the poor away from finding remunerative work and toward dependency on the state. Eliminating social safety nets can discourage free market entrepreneurs by increasing the risk of business failure from a temporary setback to financial ruin.
Taxes on the Wealthy Based on CBO Estimates, under 2013 tax law the top 1% will be paying a higher effective tax rate, while other income groups will remain essentially unchanged. CBO chart illustrating the percent reduction in income inequality due to Federal taxes and income transfers from 1979 to 2011. CBO reported that less progressive tax and transfer policies contributed to an increase in aftertax income inequality between 1979 and 2007. This indicates that more progressive income tax policies (e.g., higher income taxes on the wealthy and a higher earned-income tax credit) would reduce after-tax income inequality. Recent policies enacted under President Obama increase taxes on the wealthy, including the American Taxpayer Relief Act of 2012 and the Affordable Care Act. As reported by The New York Times in January 2014, these laws include several tax increases on individuals earning over $400,000 and couples earning over $450,000: Page 61 of 102
Raised the top marginal tax rate to 39.6% from 35%; Raised the rate on dividends and capital gains by 5 percentage points, to 20 percent; and Two new surcharges — a 3.8% tax on investment income and a 0.9% tax on regular income.
These changes are estimated to add $600 billion to revenue over 10 years, while leaving the tax burden on everyone else mostly as it was. This reverses a long-term trend of lower tax rates for upper income persons. The CBO estimated that the average tax rate for the top 1% rose from 28.1% in 2008 to 33.6% in 2013, reducing after-tax income inequality relative to a baseline without those policies. The economists Emmanuel Saez and Thomas Piketty recommend much higher top marginal tax rates on the wealthy, up to 50 percent, or 70 percent or even 90 percent. Ralph Nader, Jeffrey Sachs, the United Front Against Austerity, among others, call for a financial transactions tax (also known as the Robin Hood tax) to bolster the social safety net and the public sector. The Pew Center reported in January 2014 that 54% of Americans supported raising taxes on the wealthy and corporations to expand aid to the poor. By party, 29% of Republicans and 75% of Democrats supported this action. During 2012, investor Warren Buffett advocated higher minimum effective income tax rates on the wealthy, considering all forms of income: "I would suggest 30 percent of taxable income between $1 million and $10 million, and 35 percent on amounts above that." This would eliminate special treatment for capital gains and carried interest, which are taxed at lower rates and comprise a relatively larger share of income for the wealthy. He argued that in 1992, the tax paid by the 400 highest incomes in the United States averaged 26.4% of adjusted gross income. In 2009, the rate was 19.9%.
Corporate Tax Reform Economist Dean Baker argues that the existence of tax loopholes, deductions, and credits for the corporate income tax contributes to rising income inequality by permitting large corporations with many accountants to reduce their tax burden and by permitting large accounting firms to receive payments from smaller businesses in exchange for helping these businesses reduce their tax burden. He says that this redistributes large sums of money that would otherwise be taxed to individuals who are already wealthy yet contribute nothing to society in order to obtain this wealth. He further argues that since a large portion of corporate income is reinvested in the business, taxing Page 62 of 102
corporate income amounts to a tax on reinvestment, which he says should be left untaxed. He concludes that eliminating the corporate income tax, while needing to be offset by revenue increases elsewhere, would reduce income inequality.
Minimum Wages CBO projections of the effects of minimum wage increases on employment and income, under two scenarios In his 2013 State of the Union address, Barack Obama proposed raising the federal minimum wage. The progressive economic think tank the Economic Policy Institute agrees with this position, stating: "Raising the minimum wage would help reverse the ongoing erosion of wages that has contributed significantly to growing income inequality." In response to the fast-food worker strikes of 2013, Labor Secretary Thomas Perez said that it was another sign of the need to raise the minimum wage for all workers: "It's important to hear that voice... For all too many people working minimum wage jobs, the rungs on the ladder of opportunity are feeling further and further apart." The Economist wrote in December 2013: "A minimum wage, providing it is not set too high, could thus boost pay with no ill effects on jobs....America's federal minimum wage, at 38% of median income, is one of the rich world's lowest. Some studies find no harm to employment from federal of state minimum wages, others see a small one, but none finds any serious damage." The U.S. minimum wage was last raised to $7.25 per hour in July 2009. As of December 2013, there were 21 states with minimum wages above the Federal minimum, with the State of Washington the highest at $9.32. Ten states index their minimum wage to inflation. The Pew Center reported in January 2014 that 73% of Americans supported raising the minimum wage from $7.25 to $10.10 per hour. By party, 53% of Republicans and 90% of Democrats favored this action. Also in January 2014, six hundred economists sent the President and Congress a letter urging for a minimum wage hike to $10.10 an hour by 2016.
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In February 2014, the CBO reported the effects of a minimum wage increase under two scenarios, an increase to $10.10 with indexing for inflation thereafter and an increase to $9.00 with no indexing:
Income inequality would be improved under both scenarios. Families with income more than 6 times the poverty threshold would see their incomes fall (due in part to their business profits declining with higher employee costs), while families with incomes below that threshold would rise. Employment would likely fall by 500,000 under the $10.10 option and 100,000 under the $9.00 option, with a wide range of possible outcomes. Approximately 16.5 million workers would have their wages rise under the $10.10 option versus 7.5 million under the $9.00 option. The number of persons below the poverty income threshold would fall by 900,000 under the $10.10 option versus 300,000 under the $9.00 option.
Maximum Wage Implementation Amalgamated Transit Union international president Lawrence J. Hanley has called for a maximum wage law, which "would limit the amount of compensation an employer could receive to a specified multiple of the wage earned by his or her lowest paid employees." CEO pay at the largest 350 U.S. companies was 20 times the average worker pay in 1965; 58 times in 1989 and 273 times in 2012.
Subsidies and Income Guarantees Others argue for a Basic income guarantee, ranging from civil rights leader Martin Luther King, Jr. to libertarians such as Milton Friedman (in the form of negative income tax), Robert Anton Wilson, Gary Johnson (In the form of the fair tax "prebate") and Charles Murray to the Green Party.
Rent-Seeking Limits General limitations on and taxation of rent-seeking is popular with large segments of both Republicans and Democrats.
Economic democracy The economists Richard D. Wolff and Gar Alperovitz claim that greater economic equality could be achieved by extending democracy into the economic sphere. In an essay for Harper's Magazine, investigative journalist Erik Reece argues that "With the political right entrenched in its opposition to unions, worker-owned cooperatives represent a less divisive yet more radical model for returning wealth to the workers who earned it."
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Wealth Inequality Related to income inequality is the topic of wealth inequality, which refers to the distribution of net worth (i.e., what is owned minus what is owed) as opposed to annual income. Net worth is affected by movements in the prices of assets, such as stocks, bonds, and real estate, which can fluctuate significantly over the short-term. Income inequality also has a significant effect over long-term shifts in wealth inequality, as income is accumulated. Wealth inequality is also highly concentrated and increasing:
The top 1% owned approximately 40% of the wealth in 2012, versus 23% in 1978. The top 1% share of wealth was at or below 30% from 1950-1993. The top 0.1% owned approximately 22% of the wealth in 2012, versus 7% in 1978. The top 0.1% share of wealth was at or below 10% from 1950-1987.
The increase in wealth for the 1% was not homogeneous, with much of the wealth gains in the top 0.1%. Those between the top 1 percent and top 0.5 percent have actually lost a significant share of wealth over the past 50 years. Further, the top 400 Americans had net worth of $2 trillion in 2013, which was more than the combined net worth of the bottom 50% of U.S. households. The average net worth of these 400 Americans was $5 billion. The lower 50% of households held 3% of the wealth in 1989 and 1% in 2013. The average net worth of the bottom 50% of households in 2013 was approximately $11,000. This wealth inequality is apparent in the share of assets held. In 2010, the top 5% wealthiest households had approximately 72% of the financial wealth, while the bottom 80% of households had 5%. Financial wealth is measured as net worth minus home values, meaning incomegenerating financial assets like stocks and bonds, plus business equity. The Center for American Progress reported in September 2014 that: "The trends in rising inequality are also striking when measured by wealth. Among the top 20 percent of families by net worth, average wealth increased by 120 percent between 1983 and 2010, while the middle 20 percent of families only saw their wealth increase by 13 percent, and the bottom fifth of families, on average, saw debt exceed assets—in other words, negative net worth...Homeowners in the bottom quintile of wealth lost an astounding 94 percent of their wealth between 2007 and 2010."
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The De-Industrialization Crisis Deindustrialization is a process of social and economic change caused by the removal or reduction of industrial capacity or activity in a country or region, especially heavy industry or manufacturing industry. It is the opposite of industrialization.
Multiple Interpretations There are multiple interpretations of what this process is. Cairncross (1982) and Lever (1991) offer four possible definitions of deindustrialization: 1. A straightforward decline in the output of manufactured goods or in employment in the manufacturing sector. This, however, can be misleading because short-run or cyclical downturns may be misinterpreted as long-run deindustrialization 2. A shift from manufacturing to the service sectors, so that manufacturing has a lower share of total output or employment. This may also be misleading, however, as such a shift may occur even if manufacturing is growing in absolute terms 3. That manufactured goods comprise a declining share of external trade, so that there is a progressive failure to achieve a sufficient surplus of exports over imports to maintain an economy in external balance 4. A continuing state of balance of trade deficit (as described in the third definition above) that accumulates to the extent that a country or region is unable to pay for necessary Page 67 of 102
imports to sustain further production of goods, thus initiating a further downward spiral of economic decline Colonization of different Asian countries by European powers between 18th-20th centuries led to fall in manufacturing and further fall in global GDP share of Asian countries mainly India, China and South-East Asia.
Explanations Theories that predict or explain deindustrialization have a long intellectual lineage. Rowthorn (1992) argues that Marx's theory of declining (industrial) profit may be regarded as one of the earliest. This theory argues that technological innovation enables more efficient means of production, resulting in increased physical productivity, i.e., a greater output of use value per unit of capital invested. In parallel, however, technological innovations replace people with machinery, and the organic composition of capital increases. Assuming only labor can produce new additional value, this greater physical output embodies a smaller value and surplus value. The average rate of industrial profit therefore declines in the longer term. Rowthorn and Wells (1987) distinguish between deindustrialization explanations that see it as a positive process of, for example, maturity of the economy, and those that associate deindustrialization with negative factors like bad economic performance. They suggest deindustrialization may be both an effect and a cause of poor economic performance. Pitelis and Antonakis (2003) suggest that, to the extent that manufacturing is characterized by higher productivity, this leads, all other things being equal, to a reduction in relative cost of manufacturing products, thus a reduction in the relative share of manufacturing (provided manufacturing and services are characterized by relatively inelastic demand). Moreover, to the extent that manufacturing firms downsize through, e.g., outsourcing, contracting out, etc., this reduces manufacturing share without negatively influencing the economy. Indeed, it potentially has positive effects, provided such actions increase firm productivity and performance. George Reisman (2002) identified inflation as a contributor to deindustrialization. In his analysis, the process of fiat money inflation distorts the economic calculations necessary to operate capital-intensive manufacturing enterprises, and makes the investments necessary for sustaining the operations of such enterprises unprofitable. Institutional arrangements have also contributed to deindustrialization such as economic restructuring. With breakthroughs in transportation, communication and information technology, a globalized economy that encouraged foreign direct investment, capital mobility and labor migration, and new economic theory's emphasis on specialized factor endowments, manufacturing moved to lower-cost sites and in its place service sector and financial agglomerations concentrated in urban areas (Bluestone & Harrison 1982, Logan & Swanstrom 1990). The term de-industrialization crisis has been used to describe the decline of labor-intensive industry in a number of countries and the flight of jobs away from cities. One example is laborPage 68 of 102
intensive manufacturing. After free-trade agreements were instituted with less developed nations in the 1980s and 1990s, labor-intensive manufacturers relocated production facilities to third world countries with much lower wages and lower standards. In addition, technological inventions that required less manual labor, such as industrial robots, eliminated many manufacturing jobs.
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The War on Poverty In the US The War on Poverty is the unofficial name for legislation first introduced by United States President Lyndon B. Johnson during his State of the Union address on January 8, 1964. This legislation was proposed by Johnson in response to a national poverty rate of around nineteen percent. The speech led the United States Congress to pass the Economic Opportunity Act, which established the Office of Economic Opportunity (OEO) to administer the local application of federal funds targeted against poverty. As a part of the Great Society, Johnson believed in expanding the federal government's roles in education and health care as poverty reduction strategies. These policies can also be seen as a continuation of Franklin D. Roosevelt's New Deal, which ran from 1933 to 1935, and the Four Freedoms of 1941. The legacy of the War on Poverty policy initiative remains in the continued existence of such federal programs as Head Start, Volunteers in Service to America (VISTA), TRIO, and Job Corps. The popularity of a war on poverty waned after the 1960s. Deregulation, growing criticism of the welfare state, and an ideological shift to reducing federal aid to impoverished people in the 1980s and 1990s culminated in the Personal Responsibility and Work Opportunity Act of 1996, which, as claimed President Bill Clinton, "end[ed] welfare as we know it."
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Major Initiatives
Social Security Act 1965 (Created Medicare and Medicaid) – July 19, 1965 Food Stamp Act of 1964- August 31, 1964 The Economic Opportunity Act of 1964 which created the Community Action Program, Job Corps and Volunteers in Service to America (VISTA), centerpiece of the "war on poverty" – August 20, 1964 Elementary and Secondary Education Act - April 11, 1965
The Office of Economic Opportunity was the agency responsible for administering most of the War on Poverty programs created during Johnson's Administration, including VISTA, Job Corps, Head Start, Legal Services and the Community Action Program. The OEO was established in 1964 and quickly became a target of both left-wing and right-wing critics of the War on Poverty. Directors of the OEO included Sargent Shriver, Bertrand Harding, and Donald Rumsfeld. The OEO launched Project Head Start as an eight-week summer program in 1965. The project was designed to help end poverty by providing preschool children from low-income families with a program that would meet emotional, social, health, nutritional, and psychological needs. Head Start was then transferred to the Office of Child Development in the Department of Health, Education, and Welfare (later the Department of Health and Human Services) by the Nixon Administration in 1969. President Johnson also announced a second project to follow children from the Head Start program. This was implemented in 1967 with Project Follow Through, the largest educational experiment ever conducted. The policy trains disadvantaged and at-risk youth and has provided more than 2 million disadvantaged young people with the integrated academic, vocational, and social skills training they need to gain independence and get quality, long-term jobs or further their education. Job Corps continues to help 70,000 youths annually at 122 Job Corps centers throughout the country. Besides vocational training, many Job Corps also offer GED programs as well as high school diplomas and programs to get students into college.
Results and Aftermath Decline in welfare benefits highlights decreased support in government for War on Poverty initiatives 1962–2006 (in 2006 dollars). In the decade following the 1964 introduction of the war on poverty, poverty rates in the U.S. dropped to their lowest level since comprehensive records began in 1958: from 17.3% in the year the Economic Opportunity Act was implemented to 11.1% in 1973. They have remained between 11 and 15.2% ever since.
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The ‗absolute poverty line‘ is the threshold below which families or individuals are considered to be lacking the resources to meet the basic needs for healthy living; having insufficient income to provide the food, shelter and clothing needed to preserve health. Poverty among Americans between ages 18–64 has fallen only marginally since 1966, from 10.5% then to 10.1% today. Poverty has significantly fallen among Americans under 18 years old from 23% in 1964 down to less than 17%, although it has risen again to 20% in 2009. The most dramatic decrease in poverty was among Americans over 65, which fell from 28.5% in 1966 to 10.1% today. In 2004, more than 35.9 million, or 12% of Americans including 12.1 million children, were considered to be living in poverty with an average growth of almost 1 million per year. According to the Cato Institute, a libertarian think tank, since the Johnson Administration almost $15 trillion has been spent on welfare, with poverty rates being about the same as during the Johnson Administration. A 2013 study published by Columbia University asserts that without the social safety net, the poverty rate would have been 29% for 2012, instead of 16%. According to OECD data from 2012, the poverty rate before taxes and transfers was 28.3%, while the poverty rate after taxes and transfers fell to 17.4%. The OEO was dismantled by President Nixon in 1973, though many of the agency's programs were transferred to other government agencies. According to the "Readers' Companion to U.S. Women's History", Many observers point out that the War on Poverty's attention to Black America created the grounds for the backlash that began in the 1970s. The perception by the white middle class that it was footing the bill for ever-increasing services to the poor led to diminished support for welfare state programs, especially those that targeted specific groups and neighborhoods. Many whites viewed Great Society programs as supporting the economic and social needs of low-income urban minorities; they lost sympathy, especially as the economy declined during the 1970s. United States Secretary of Health, Education, and Welfare under President Jimmy Carter, Joseph A. Califano, Jr. wrote in 1999 in an issue of the Washington Monthly that: "In waging the war on poverty, congressional opposition was too strong to pass an income maintenance law. So LBJ took advantage of the biggest automatic cash machine around: Social Security. He proposed, and Congress enacted, whopping increases in the minimum benefits that lifted some two million Americans 65 and older above the poverty line. In 1996, thanks to those
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increased minimum benefits, Social Security lifted 12 million senior citizens above the poverty line ... No Great Society undertaking has been subjected to more withering conservative attacks than the Office of Economic Opportunity. Yet, the War on Poverty was founded on the most conservative principle: Put the power in the local community, not in Washington; give people at the grassroots the ability to stand tall on their own two feet. Conservative claims that the OEO poverty programs were nothing but a waste of money are preposterous ... Eleven of the 12 programs that OEO launched in the mid-'60s are alive, well and funded at an annual rate exceeding $10 billion; apparently legislators believe they're still working."
Reception and Critique Number in Poverty and Poverty Rate: 1959 to 2011. United States. President Johnson's "War on Poverty" speech was delivered at a time of recovery (the poverty level had fallen from 22.4% in 1959 to 19% in 1964 when the War on Poverty was announced) and it was viewed by critics as an effort to get the United States Congress to authorize social welfare programs. Some economists, including Milton Friedman, have argued that Johnson's policies actually had a negative impact on the economy because of their interventionist nature, noting in a PBS interview that "the government sets out to eliminate poverty, it has a war on poverty, so-called "poverty" increases. It has a welfare program, and the welfare program leads to an expansion of problems. A general attitude develops that government isn't a very efficient way of doing things." Adherents of this school of thought recommend that the best way to fight poverty is not through government spending but through economic growth. Prof. Tony Judt, the late historian, said in reference to the earlier proposed title of the Personal Responsibility and Work Opportunity Act that "a more Orwellian title would be hard to conceive" and attributed the decline in the popularity of the Great Society as a policy to its success, as fewer people feared hunger, sickness, and ignorance. Additionally, fewer people were concerned with ensuring a minimum standard for all citizens and social liberalism. Conservative Research Fellow at the Independent Institute James L. Payne followed this line of thinking when he wrote that "the war on poverty was a costly, tragic mistake [because]...abolishing poverty did not seem far-fetched to the activists ... [and] it was a perspective that led to intolerance ... The simple economic theory of poverty led to a single underlying principle for welfare programs ... In adopting the handout approach for their programs, the war-on-poverty activists failed to notice—or failed to care—that they were Page 74 of 102
ignoring over a century of theory and experience in the social welfare field ... The war-onpoverty activists not only ignored the lessons of the past on the subject of handouts; they also ignored their own experience with the poor." Others took a different tack. In 1967, in his book Where Do We Go from Here: Chaos or Community? Martin Luther King "criticized Johnson's War on Poverty for being too piecemeal," saying that programs created under the "war on poverty" such as "housing programs, job training and family counseling" all had "a fatal disadvantage [because] the programs have never proceeded on a coordinated basis...[and noted that] at no time has a total, coordinated and fully adequate program been conceived." In his speech on April 4, 1967 at Riverside Church in New City, King connected the war in Vietnam with the "war on poverty": "There is at the outset a very obvious and almost facile connection between the war in Vietnam and the struggle I, and others, have been waging in America. A few years ago there was a shining moment in that struggle. It seemed as if there was a real promise of hope for the poor -- both black and white -- through the poverty program. There were experiments, hopes, new beginnings. Then came the buildup in Vietnam and I watched the program broken and eviscerated as if it were some idle political plaything of a society gone mad on war, and I knew that America would never invest the necessary funds or energies in rehabilitation of its poor so long as adventures like Vietnam continued to draw men and skills and money like some demonic destructive suction tube. So I was increasingly compelled to see the war as an enemy of the poor and to attack it as such. Perhaps the more tragic recognition of reality took place when it became clear to me that the war was doing far more than devastating the hopes of the poor at home." This criticism was repeated in his speech at the same place later that month when he said that "and you may not know it, my friends, but it is estimated that we spend $500,000 to kill each enemy soldier, while we spend only fifty-three dollars for each person classified as poor, and much of that fifty-three dollars goes for salaries to people that are not poor. So I was increasingly compelled to see the war as an enemy of the poor, and attack it as such." The next year, King started the Poor People's Campaign to address the shortcomings of the "war on poverty" and to "demand a check" for suffering African-Americans which was carried on briefly after his death with the construction and maintenance of an encampment, Resurrection City, for over six weeks. Years later, a writer in The Nation remarked that "the war on poverty has too often been a war on the poor themselves," but that much can be done. In 1989, the former executive officer of the Task Force on Poverty Hyman Bookbinder addressed such criticisms of the "war on poverty" in an op-ed in The New York Times. He wrote that:
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"Today, the ranks of the poor are again swelling ... These and other statistics have led careless observers to conclude that the war on poverty failed. No, it has achieved many good results. Society has failed. It tired of the war too soon, gave it inadequate resources and did not open up new fronts as required. Large-scale homelessness, an explosion of teen-age pregnancies and single-parent households, rampant illiteracy, drugs and crime these have been both the results of and causes of persistent poverty. While it is thus inappropriate to celebrate an anniversary of the war on poverty, it is important to point up some of the big gains ... Did every program of the 60's work? Was every dollar used to its maximum potential? Should every Great Society program be reinstated or increased? Of course not ... First, we cannot afford not to resume the war. One way or another, the problem will remain expensive. Somehow, we will provide for the survival needs of the poorest: welfare, food stamps, beds and roofs for the homeless, Medicaid. The fewer poor there are, the fewer the relief problems. Getting people out of poverty is the most costeffective public investment." In March 3, 2014, as Chairman of the Budget Committee of the House of Representatives, Paul Ryan released his "The War on Poverty: 50 Years Later" report, asserting that some of 92 federal programs designed to help lower-income Americans have not provided the relief intended and that there is little evidence that these efforts have been successful. At the core of the report are recommendations to enact cuts to welfare, child care, college Pell grants and several other federal assistance programs. In the appendix titled "Measures of Poverty", when the poverty rate is measured by including non-cash assistance from food stamps, housing aid and other federal programs, the report states that these measurements have "implications for both conservatives and liberals. For conservatives, this suggests that federal programs have actually decreased poverty. For liberals, it lessens the supposed need to expand existing programs or to create new ones." Several economists and social scientists whose work had been referenced in the report said that Ryan either misunderstood or misrepresented their research.
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The Robin Hood Foundation The Robin Hood Foundation is a charitable organization which attempts to alleviate problems caused by poverty in New York, United States. The organization also administers a relief fund for disasters in the New York City area. Founded in 1988, Robin Hood was the brainchild of hedge fund manager Paul Tudor Jones. Peter Borish was also a founding board member of the organizaion. The foundation combines investment principles and philanthropy to assist programs that target poverty in New York City. In 2006, the board of directors included such names as Jeffrey Immelt, Diane Sawyer, Harvey Weinstein, Marie-Josee Kravis, Lloyd Blankfein, of Goldman Sachs, Richard S. Fuld, Jr., formerly of Lehman Brothers, Glenn Dubin, of Highbridge Capital, Marian Wright Edelman and actress Gwyneth Paltrow. Funding for the organization's activities comes from donations and fund raising efforts. In 2009, George Soros gave the foundation a US$50 million contribution. The money reportedly helped the organization raise significantly more than that amount.[7] In 2001 the The Concert for New York City provided funds for the organization. After Hurricane Sandy, the 12-12-12: The Concert for Sandy Relief concert also provided funds for the foundation's efforts.[8] Artists including The Rolling Stones, Robert Plant & The Strange Sensation, Shakira, John Legend, The Black Eyed Peas, Lady Gaga, The Who and Aerosmith have performed at the group's annual fund raising galas.
Approach According to Fortune Magazine, "Robin Hood was a pioneer in what is now called venture philanthropy, or charity that embraces free-market forces. An early practitioner of using metrics to measure the effectiveness of grants, it is a place where strategies to alleviate urban poverty are hotly debated, ineffectual plans are coldly discarded, and its staff of 66 hatches radical new ideas."[2] More specifically, the foundation states that it applies the following principles:
Give 100 percent of every donation directly to programs helping poor New Yorkers. Identify and stop poverty at its roots. Protect and leverage Robin Hood‘s investments by using sound business principles to help programs become more effective. Use metrics and qualitative data to evaluate programs and measure results to compare the relative poverty-fighting success of similar programs.
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Programs The Robin Hood Foundation works with more than 240 nonprofit organizations in New York and surrounding areas. They categorize their programs into "Core fund recipients" and "Relief fund recipients". Core fund recipients consist of four portfolios: early childhood, education, jobs and economic security, and survival. Relief fund activities established to assist low income victims of the 9/11 attacks addressed employment, lower income victims' services and relief and mental health services in addition to other grants. The relief fund also benefited victims of Hurricane Sandy.
Reception The Robin Hood Foundation was featured in Fortune's 18 September 2006 issue, where the article states that the foundation is "one of the most innovative and influential philanthropic organizations of our time."
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The Tipping-Point Community Tipping Point Community is a grant-making organization that fights poverty in the San Francisco Bay Area. Founded by Daniel Lurie in 2005, Tipping Point screens non-profits rigorously to find, fund and partner with the most promising groups connecting Bay Area individuals and families to the opportunities needed to break the cycle of poverty and achieve economic self-sufficiency. Tipping Point provides this management assistance through the strategic partnerships it builds with private sector corporations and non-profit organizations. Tipping Point is modeled after the Robin Hood Foundation in New York, and has been praised by the San Francisco Chronicle for giving "young donors a place to make their first foray into the charity world by taking on issues of poverty and class." According to the San Francisco Business Times, "Tipping Point has quadrupled the number of donor gifts and increased the amount it gives away from $450,000 two years ago to $5.5 million this year." Its board is composed of local philanthropists including Katie Schwab, Chris James and former San Francisco 49er Ronnie Lott. The board underwrites all fundraising and operating costs, enabling 100% of every dollar donated to go directly toward grant-making. Since 2005, Tipping Point has raised more than $60 million to support nearly 250,000 Bay Area people in need. There‘s no one way to fight poverty. We fund the most effective poverty-fighting organizations across six Bay Area counties that provide comprehensive support in four key portfolio areas:
Education + Youth Employment Family Wellness Housing
Special Initiatives
SingleStop USA – at the 2008 Benefit, Tipping Point raised $1.5 M to seed 6 SingleStop sites in the Bay Area. SingleStop is a national program that connects low-income families with untapped public benefits, tax credits and essential services. Slate Magazine recently called SingleStop "the best poverty fighting bet."
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References ______
1. http://en.wikipedia.org/wiki/Poverty_in_the_United_States 2. http://en.wikipedia.org/wiki/Income_inequality_in_the_United_States 3. http://en.wikipedia.org/wiki/The_Spirit_Level:_Why_More_Equal_Societies _Almost_Always_Do_Better 4. http://en.wikipedia.org/wiki/Deindustrialization 5. http://en.wikipedia.org/wiki/Robin_Hood_Foundation 6. http://en.wikipedia.org/wiki/Tipping_Point_Community 7. http://en.wikipedia.org/wiki/War_on_Poverty 8. http://en.wikipedia.org/wiki/Robin_Hood_Foundation 9. http://en.wikipedia.org/wiki/Tipping_Point_Community 10.http://www.vanneman.umd.edu/socy789b/hoynesps06.pdf 11.http://povertyinamerica.mit.edu/download/atlas_of_poverty_in_america_p1. pdf 12.http://web.stanford.edu/group/scspi/sotu/SOTU_2014_CPI.pdf
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Attachment A Poverty in America: Trends and Explanations
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Journal of EconomicPerspectives-Volume20, Number1-Winter 2006-Pages 4 7-68
Poverty in America: Trends and Explanations Hilary W. Hoynes, Marianne E. Page and Ann Huff Stevens
ver the past 45 years, the United States has experienced a rising standard of living, with real GDP per capita more than doubling between 1959 and 2004. In contrast, living standards among some groups seem to have stagnated. The nonelderly poverty rate declined from 1959-1969, but then rose from 10.7 percent in 1970 to 12.7 percent in 1980 and remained at 12.8 percent in 2003. Figure 1 illustrates the trends in GDP per capita and poverty over this period. Although a number of studies have documented a correlation between macroeconomic conditions and poverty, Figure 1 makes clear that the relationship is not as simple, or as strong, as one might think. What additional factors can explain the starkly different trends in economic well-being that are measured by overall GDP growth and the poverty rate1 Consideration of additional factors only adds to the puzzle. First, the fraction of women ages 25 to 64 participating in the labor force and contributing to household money income skyrocketed during this period, increasing from 57 percent to 76 percent between 1970 and 2000 according to data from the Current Population Survey. At the same time, average levels of education grew substantially. In 1970, 48 percent of individuals over age 25 had less than a high school education; by 2000 this figure had fallen to 17 percent (U.S. Bureau of the Census, 2004). Finally, the stickiness in the nonelderly poverty rate does not exist for all demographic groups in the United States: poverty rates among the elderly
O
m Hilary W. Hoynes is Professorof Economics,Marianne E. Page is AssociateProfessorof Economics,and Ann Huff Stevensis AssociateProfessorof Economics,all at Universityof Californiaat Davis, Davis, California.Theire-mailaddressesare(hwhoynes@ucdavis.edu), and (annstevens@ucdavis.edu), (mepage@ucdavis.edu) respectively.
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Figure 1 Trends in Individual Poverty Rates and Real GDP per Capita, 1959-2003 All
40-
Nonelderly Children
35
Elderly GDP per capita
-40,000 S35,000
30-
-30,000
25rate 20
-25,000 S20,000 -,
Poverty 15-
-15,000
10 -
10,000
5-
(20031) capita per GDP
-5,000
0-
-0 1963
1968
1973
1978
1983
1988
1993
1998
2003
Source:Poverty rates are from U.S. Bureau of the Census, Current Population Survey, Annual Social and Economic Supplements. The GDP per capita series is from the Economic Report of the President (2005). Note:The poverty rate data are unavailable for some subgroups for 1960-1965.
declined steadily during this period, falling from 24.6 percent in 1970 to 10.2 percent in 2003. Other factors may better explain why the poverty rate has failed to fall. Rising numbers of female headed families may offset income gains from women's increasing labor force participation. Increasing income inequality-in particular stemming from declines in wages for less-skilled workers-may have limited the povertyfighting effects of economic growth. Finally, the level of and changes in government benefits directed toward the nonelderly may explain why the nonelderly poverty rate has not moved in the same direction as elderly poverty. Our task in this paper is to document and quantify the effects of these competing factors to understand recent poverty trends better. Since the steady fall in elderly poverty rates in recent decades is likely explained by other factors such as Social Security (Englehardt and Gruber, 2004), we focus throughout this paper on the conundrum of why the nonelderly poverty rate has failed to decline as the economy has expanded.
Dimensions of Poverty In this section, we summarize some basic facts about poverty in the United States, relying on a combination of previously published data from the Census
Hilary W. Hoynes,MarianneE. Page and Ann Huff Stevens 49
Bureau and our own tabulations based on Current Population Survey data. Throughout the paper, we measure individual poverty rates (the alternative is to measure poverty rates among families) using the official Census Bureau definition. In particular, an individual is considered poor if their total family pretax money income in a given year is below the poverty threshold for their family size and age composition. By construction, all persons in the same family have the same poverty status. In 2004, the poverty threshold for a family of four was roughly 119,000, and for a single individual it was approximately 110,000. For details about poverty rates and how they are calculated, a useful starting point is the website of the U.S. Census Bureau at (http://www.census.gov/hhes/www/poverty/poverty.html). A Snapshot of Current Poverty Data on poverty in the United States is collected annually by the Current Population Survey. In 2003, 12.8 percent of all nonelderly individuals lived below the poverty line, while 17.6 percent of children lived in families with incomes below the poverty line. Women are more likely to be poor than men; in 2003, the poverty rate for males was 11.7 percent and for females was 13.9 percent. This relatively small difference is driven by the fact that men and women live together in most families and so have the same family income and poverty standard. When the population is divided using characteristics of the head of household or family structure, the differences are more dramatic. The poverty rate for individuals for whom the head of the family is married was 7 percent. In contrast, among individuals in families with an unmarried head and children present (five-sixths of whom are female unmarried heads), the poverty rate was 40.3 percent. Finally, among those with single heads, but no children present, the 2003 poverty rate was 17.9 percent. Race and ethnicity are also strongly related to the probability of living in poverty. The 2003 poverty rates among blacks and Hispanics were 24.3 percent and 22.5 percent, respectively, nearly triple the 8.2 percent poverty rate for whites. Individuals born in the United States have a poverty rate of 11.8 percent, while those who are immigrants have a rate of 17.4 percent. Finally, education is a strong predictor of poverty status. Among individuals living in families in which the head has less than a high school education, 31.3 percent are below the poverty line, compared with just 9.6 percent of those whose head has at least a high school education. Table 1 lists some characteristics of the poor and for comparison also shows the characteristics for the general population. The first row of Table 1 shows that the poor as a group are younger than the population as a whole, with children making up 39.8 percent of the poor, compared with 28.8 percent of the overall population. The slightly higher poverty rates among women, who are roughly half of the population, of course mean that the poor are also disproportionately female. The poor are disproportionately comprised of single parents with children. Single
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Table 1 Characteristics of the Nonelderly Poor, 2003 (percentagewith given characteristic) Among nonelderlypoor
Among all nonelderly
Individual characteristics Age <18 Male Female Family head is Married Single with kids Single without kids White Black Hispanic Family head's education <High school Native-born Immigrant Head worked last year
39.81 45.51 54.51
28.81 49.81 50.21
35.01 39.11 25.81 42.21 24.11 26.81
66.61 14.41 18.91 65.71 12.61 15.11
35.31 82.61 17.41 50.01
14.41 87.41 12.61 81.11
Source:Author's tabulations of the 2004 March CPS. Note: The age, gender, race and ethnicity are assigned using the individual's characteristics. Family type, immigrant status, education and employment are assigned based on characteristicsof the head of the family.
parent families comprise 39.1 percent of the poor, although persons in such families make up only 14.4 percent of the total population. The racial and ethnic composition of the poor is disproportionately minority, but the modal poor individual is a white non-Hispanic. In 2003, 42.2 percent of the poor were white, 24.1 percent black and 26.8 percent Hispanic. In the overall population, whites make up 65.7 percent, blacks make up 12.6 percent, and Hispanics 15.1 percent. Immigrants are 17.4 percent of the poor. The bottom row of Table 1 shows that half of the poor were in a familywhose household head worked in the past year. In the population overall, 81 percent of household heads worked. Persistence of Poverty One dimension of poverty that cannot be captured using data from the Current Population Survey is its persistence, since the CPS only asks about income in a given year and does not ask about individuals' income history. Bane and Ellwood (1986) provide a fundamental contribution to our understanding of the dynamics of poverty. In particular, imagine that during a calendar year one family is poor for all 12 months and 12 other families are poor for only one month each. At any given time, two families are poor, and half of those who are poor at any given time are poor for the long term. But over the course of a year, only one of the 13 families who experienced at least one month of poverty were poor for an
Povertyin America:Trendsand Explanations 51
extended time. Thus, measures of how the persistence of poverty is distributed are quite different if the analyst considers a "flowmeasure," consisting of all individuals who have experienced a spell of poverty, or if the analyst considers a "stock measure" of all individuals who are poor at a point in time. Stevens (1999) presents calculations of the persistence of poverty that take into account that among those who leave poverty in a given year, there is substantial re-entry in future years. Using data from the 1968 through 1988 waves of the Panel Study of Income Dynamics (PSID), Stevens shows that approximately 35 percent of individuals beginning a spell of poverty will be poor for at least five of the next ten years, with about half of these occurring across multiple spells of poverty. Stevens also presents information on how the persistence of poverty varies with individual and family characteristics. She finds that there are large differences in the persistence of poverty by race, education of the family head and family structure. For example, a 20 year-old black woman with less than a high school education has a 64.1 percent chance of being poor in at least five of the next 10 years, whereas the comparable figure for a 20 year-old white woman is 39.6 percent. In general, children who are born into poverty face a greater likelihood of remaining poor than do young adults beginning a period of poverty. For example, a one-year-old black child living in a female-headed family in which the head has less than a high school education has an 89.5 percent chance of being poor in five or more of the next ten years; but a white child born into a similar family setting has a 63 percent chance of being poor for five or more of the next ten years. Measuring Poverty The statistics presented in this paper are based on the official definition of poverty in the United States, which reflects the fraction of persons (or families) with incomes below an absolute threshold.' The poverty thresholds were developed in 1963-1964 by Mollie Orshansky, an economist at the Social Security Administration, and were adopted in August 1969 (Fisher, 1992). They were constructed by first estimating the cost of the Department of Agriculture's "economy food plan" for different family sizes. Tabulations from the 1955 Household Food Consumption Survey showed that on average, one-third of family after-tax income was spent on food, so the estimated food costs were then multiplied by three to construct the poverty thresholds for households of different sizes (a higher multiplier was used for families with less than three persons to reflect the high fixed costs of housing). These thresholds have been adjusted each year to reflect changes in the cost of living using the Consumer Price Index (CPI), but otherwise, the official poverty
1
The main conceptual alternative to the official U.S. poverty measure used is relativepoverty,which measures the fraction of persons or families with income below some societal benchmark like 50 percent of median income. When using relative poverty lines, a general increase in income will not reduce poverty. Relative measures of poverty are common in international comparisons, as in the paper by Timothy Smeeding in this issue.
52 JournalofEconomic Perspectives
measure has changed little since it was created in 1969.2 In 2003, the poverty line was essentially three times the 1967 cost of the 1967 economy food plan, multiplied by the change in the CPI. Although poverty can be measured in ways other than the official definition, our work, and the work of others, shows that most of these different ways will alter the level of poverty but not the trend. For example, the economic unit used by the Census is the family-which is defined as all persons living in a household who are related by birth, marriage or adoption. Thus, households can consist of multiple families. If a couple with a child cohabitate instead of marrying, then poverty is calculated separately for the mother-and-child "family"and the father "family."If a woman and her child move in with her parents, then they are treated as a single family. To address the possible biases due to changes in family structure and living arrangements, we created a household poverty rate and a "little"family poverty rate (which splits up extended families living in the same household into separate "little" families) and found that the trends for these alternative poverty rates are very similar to the trend for the official definition. Another method of calculating poverty is to go beyond before-tax money income and include in-kind government benefits such as food stamps and housing subsidies, along with the Earned Income Tax Credit (EITC), which provides cash transfers to low-income working families as part of the tax system. Alternative measurements that include these income sources show lower poverty rates compared with official statistics-but again, the trend in poverty rates is quite similar across the official and alternative measures (Short, Garner, Johnson and Doyle, 1999). We will return to this issue below. In 1995, a report by the National Research Council made a number of recommendations for updating poverty measurement in the United States (Citro and Michael, 1995). The panel recommended updating the measure of family resources to include the value of near-cash in-kind benefits (such as food stamps, housing subsidies, school lunch and energy assistance) and to subtract income taxes, payroll taxes, out of pocket medical costs, work expenses and child care expenses. The panel also made recommendations for changing poverty thresholds, including relying on expenditure data on food, clothing and shelter, allowing for geographical variation and updating the threshold each year by changes in spending in these three areas (as opposed to adjusting by overall inflation levels). The panel's report generated significant discussion, but has not led to changes in the official poverty measure.
2
Poverty thresholds are now created for family sizes of one to nine or more persons and vary depending on the number in the family that are less than 18 and, if a one- or two-person family, whether the head is over 65. Up until 1981, separate thresholds were also provided for farm and nonfarm families and for different family types (female-headed household or not).
Hilary W. Hoynes,MarianneE. Page and Ann Huff Stevens 53
What Explains Trends in Poverty Rates1 We discuss and evaluate four determinants of changes in the poverty rate that have been advanced in the literature: the impact of labor market opportunities; the role of changes in family structure; the role played by government antipoverty programs; and the role of immigration. Labor Market Opportunities, Inequality and Macroeconomic Cycles The literature on the causes of poverty consistently cites the importance of labor market opportunities. Some focus on the poverty rate's cyclical nature (Hines, Hoynes and Krueger, 2001, 2005; Hoynes, 2000). Others identify three separate factors associated with labor market opportunities-growth, inequality and macroeconomic cycles-and explore their contribution to poverty (Blank and Card, 1993; Danziger and Gottschalk, 1995, 2004; Freeman, 2001; Gottschalk, 1997). Our analysis builds on this literature and captures these factors with four labor market measures: unemployment rates, real median wages, inequality and female employment rates. We begin by presenting the trends in these measures of labor market opportunities over the period 1967-2003. We then go on to estimate the importance of the different labor market variables in a multivariate regression model. All statistics are calculated using the Current Population Survey. Figure 2 presents the trends in poverty, unemployment rates and real median wages from 1967-2003.3 The figure documents a strong cyclical component in the poverty rate-with relatively higher poverty rates in high unemployment periods such as 1971, 1975, 1983 and 1993. However, the rise in poverty that is associated with increasing unemployment rates is lower during the early 1970s than in the 1980s and 1990s. Periods of falling poverty rates also correspond to periods during which median wages are increasing (like 1967-1973, 1983-1986, 1996-1999). Figure 3 presents trends in the poverty rate and inequality. Our measure of inequality is the ratio of the median wage to the wage at the 20th percentile.4 This measure recognizes that inequality at the low end of the distributionis what matters for poverty, while acknowledging that increases in inequality are not exclusively driven by wage declines at the bottom. The patterns here are less striking, but it appears that periods of falling inequality (like 1987-1990, 1991-1996) are also periods of falling poverty. We will argue that the virtuallycontinuous increase in wage inequality below the median is an important explanation for the upward drift in poverty rates, which
Our Our median wage measure is based on all men working full time. The enormous rise in women's labor force participation during this time period may have led to significant changes in the composition of the working population. We wanted changes in our wage measures to reflect changes in the return to work, rather than changes in the characteristics of the median worker. 4 The 20th percentile wage, W, is the wage for which 20 percent of the working population has a wage that is equal or lower than W. As with the median wage, the 20th percentile wage is taken over all men working full time.
54
Journal of EconomicPerspectives
Figure 2 Nonelderly Poverty Rates, Unemployment Rates and Median Wages, 1967-2003 Poverty rate Unemployment rate - Real median wage
0.20-
1000 -950
rate 0.15 -
-900
(20031)
-850 poverty
-800
rate,0.10
S750
earnings
-700
weekly
-650
real
0.05 -600 Unemployment 0.00 1967
-550
1971
1975
1979
1983
1987
1991
1995
Median
500 1999 2003
Source:Authors' tabulations of the 1968-2004 March CPS. Notes:Median hourly wages are defined for all full-time working men. See text for more details.
Figure3 Nonelderly Poverty Rates and Inequality, 1967-2003 0.200
Nonelderly poverty rate Median wage/20th percentile wage
- 2.000
0.175 0.150
- 1.750
rate
percentile wage
0.125Poverty 0.100
- 1.500
wage/20th weekly weekly
0.075 Median - 1.250 0.050 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 Source:Authors' tabulations of the 1968-2004 CPS. Notes:Inequality is measured as the ratio of median weekly wage to the 20th percentile weekly wage. Wages are defined using all full-time working men. See text for more details.
Povertyin America:Trendsand Explanations 55
confirms other studies that give a leading role to the changing wage distribution (Blank, 1993; Blank and Card, 1993; Freeman, 2001; Gottschalk and Danziger, 2003). Any consideration of trends in U.S. labor market opportunities over the past 40 years must include some discussion of the rise in women's labor force participation. Figure 4 shows trends in the poverty rate and female employment, which we measure as the fraction of women 25-64 who worked at all during the calendar year. Increases in women's labor force participation are expected to reduce poverty rates-as more women work, family income rises. The figure shows that this expected inverse relationship between female employment and poverty is clear in the post-1980 period, but not the pre-1980 period. Of course, these figures do not account for other possible influences that may be correlated with labor market trends. To address this possibility, we build on the existing literature, which uses both cross-section and time-series variation to identify the effects of labor market factors.5 This approach allows us to take advantage of substantial variation in business cycles and labor market opportunities both across areas and over time. Our cross-sectional variation is at the regional level, using the nine divisions defined by the Census Bureau (New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain and Pacific) and our data come from the 1968-2004 March Current Population Survey (CPS), which provides information on employment, earnings and income for the prior calendar year. Each surveyyear contains information on approximately 150,000 persons. With nine divisions and 37 years, our sample consists of 333 observations. We begin by estimating the following model, which relates poverty rates to labor market opportunities: Povratej,= a +
P3uratejt+ 32ln(medwagej,)+ P3ln(p50,,/p20t) + yj/+ r + ejt,
where Povratejt is the poverty rate for all persons under age 65 in division j in year t. Following Figures 2-4, we control for macroeconomic cycles with the unemployment rate, uratejtand use the real median weekly wage In( medwagejt)to control for
An important issue that arises throughout this literature is whether one should use national or regional (division, state, metropolitan area) controls for labor market variables. The main appeal of using national data is that variables are measured precisely and they reflect movements in the aggregate economy. However, the principle weakness of using aggregate data is that they may pick up the influences of unmeasured aggregate variables. In contrast, using regional variation in labor market opportunities leads to an increase in the size of the estimation sample and allows for the estimation of models with unrestricted time effects. The time effects control for the unmeasured aggregate variables that are a concern in the aggregate models. (It is possible, however, that controlling for these time effects in a regional regression can absorb some national trends in labor market variables.) Furthermore, some argue that labor market outcomes are more influenced by local variables than national variables (Blanchflower and Oswald, 1994; Bartik, 1994). 5
56
Journal of EconomicPerspectives
Figure 4 Nonelderly Poverty Rates and Female Employment Rates, 1967-2003 0.200
-0.80
Nonelderly poverty rate Female 25-64 employment rate
0.175
-0.75
0.150
-0.70
0.125
- 0.65
Poverty 0.100
-0.60
0.075
-0.55
rate
rate employment
Female
0.050 1967
1971
1975
1979
1983
1987
1991
1995
-0.50 1999 2003
Source:Authors' tabulations of 1968-2004 CPS. Note:Female employment rates are calculated as the fraction of women age 25-64 who worked at all during the calendar year.
overall income and growth in the economy.6 As above, our measure of inequality is the ratio of the median weekly wage to the 20th percentile of the weekly wage, ln( p50/p20)j,. Our growth and inequality measures are both specified in logs, and weekly wages are constructed by dividing annual earnings by weeks worked.' The model also controls for division fixed effects y and year fixed effects qt. This The median wage variable provides a measure of the price of labor, but it is probably not the best way to capture growth in personal income that follows the rise in GDP/capita shown in Figure 1. Median income would come closer to capturing this phenomenon. At the same time, income measures reflect both opportunities and individual choices (such as hours of work), and so it may be less appropriate to use them to "explain" trends in poverty. Nonetheless, replacing the median wage with median family income has virtually no effect on our results. 7 Here are some additional details of data construction. For the poverty data, we use the simplified poverty thresholds implemented in 1981 to construct the poverty thresholds for years prior to 1981. This adjustment reflects changes in the CPI whereas the actual thresholds prior to 1981 also varied by farm/nonfarm status and family structure. For the unemployment variable, we use the March CPS sample because Local Area Unemployment statistics from the Bureau of Labor Statistics do not begin until 1975. For median wages, for survey years 1975 and earlier, the weeks worked variable in the CPS is given within six intervals. We impute weeks within the intervals by assigning the empirical mean within the interval from 1976 (the first year with continuous weeks worked). In calculating median earnings, we drop men with weekly earnings less than 1128 (in 2003 dollars). For this full-time working sample, this is equivalent to having an hourly wage of 13.18/hour (in 2003 dollars). This is done to eliminate obvious measurement error. We also drop self-employed individuals, those working without pay or in the military, observations with negative weights and those with very low wages. 6
Hilary W. Hoynes,MarianneE. Page and Ann Huff Stevens 57
Table 2 Regression Estimates of the Impact of Labor Market Opportunities on Poverty Rates, Division Level Analysis 1967-2003
1967-1979
1980-2003
1967-2003
1967-1979
1980-2003
0.453*** (0.056)
0.898*** (0.150)
0.603*** (0.059)
0.458*** (0.061)
0.934*** (0.159)
0.494*** (0.061)
Ln(real medianweekly -0.145***
-0.251***
-0.124***
-0.145***
-0.229***
-0.113***
Unemployment rate
wage)
Ln(median/20th percentile) Fraction of women working (decimal) Constant Year fixed effects Division fixed effects Observations R-squared
(0.017)
0.262***
(0.060)
0.266***
(0.017)
0.102***
(0.021)
(0.036)
(0.022)
0.943*** (0.115) X X 333 0.91
1.612*** (0.393) X X 117 0.94
0.833*** (0.112) X X 216 0.93
(0.017)
0.262*** (0.020) 0.010 (0.038) 0.938*** (0.116) X X 333 0.91
(0.062)
0.258*** (0.037) 0.089 (0.090) 1.417*** (0.424) X X 117 0.94
(0.017)
0.095*** (0.020) -0.187*** (0.038) 0.900*** (0.115) X X 216 0.93
Source:Authors' tabulations of the 1968-2003 March CPS. Notes:Observations are division-year cells and cover 1967-2004. All dollar figures are in 2003 dollars. Regressions are weighted using division population. Robust standard errors are in parentheses. *** indicates that estimates are significant at the 1 percent level.
effectively purges our estimates from omitted variables bias resulting from variables common to all regions that are changing over time (such as changing rates of female headship) or fixed differences across geographic areas (such as differences in immigrant shares) that might also influence the poverty rate. The results of this exercise are presented in the first three columns of Table 2. The results in the first column of Table 2 are for the full 1967-2003 period. All of the labor market variables are substantive and significant at the 1 percent level. Specifically, the estimates in column 1 imply that an increase in the unemployment rate of 1 percentage point increases the poverty rate by about 0.5 percentage points, a 10 percent increase in the median wage decreases the poverty rate by about 1.5 percentage points, and a 10 percent increase in the 50-20 ratio (approximately the increase that occurred between 1975 and 1985) leads to an increase in the poverty rate of approximately 2.5 percentage points. The second and third columns show how the impact of labor market opportunities has changed over time. Initially, we looked at three periods: 1967-1979, 1980-1989 and 1990-2003, which roughly coincide with the calendar decades and include (in each period) a combination of boom and bust years. The results for the 1980s and 1990s are very similar, however, so we have combined them for ease of presentation. The difference in the estimates across the second and third columns shows quite strikingly that the impact of the labor market on poverty has weakened over time. In the later period, the estimated coefficients on the labor market
58
Journal of EconomicPerspectives
variables are roughly half of their estimated values in 1967-1979. Blank (1993) also notes that the effect of economic growth (measured by growth in real GNP) fell substantially during the 1980s, because growth in the 1980s consisted of stagnant median wages and growing wage inequality. Our results take this finding a step further: even after controlling for both median wage growth and inequality at the bottomof the distribution, we see a dramatic reduction in the relationship between labor market variables and the poverty rate. An interesting question is why the predictive power of these different labor market variables seems to be changing over time. One possibility stems from the rise in female employment-as more women work, the shock to total household income associated with events like a husband's job loss may decline. To explore further the impact of these labor market variables, we use the estimates for the full sample period (column 1) to produce counterfactual estimates of what the poverty rate would have been in each year if our labor market variables had been the only factors that had changed over time. Figure 5 shows this prediction along with the actual poverty rate. The figure makes clear that we should not be surprised that poverty rates failed to fall from 1967 through 2003. Rather, we should be surprised that they did not increase by more! Figure 5 also shows similar predictions that were created using the estimated coefficients from the 1980-2003 period, since projections based on the full sample period will not reflect the apparent change in the relationship that occurred around 1980. As it turns out, labor market variables do a very good job of predicting the poverty rate after 1980. The counterfactuals produced by this exercise are very close to actual poverty rates. The estimates presented in Table 2 and Figure 5 ignore the potentially offsetting increase in women's labor force participation illustrated in Figure 4. We did not include women's labor force participation rates in our initial model since they may reflect choices (and so be a function of the poverty rate), rather than reflecting primarily prices or constraints like our measures of unemployment and wages. To examine the importance of the trend toward increasing female employment, however, we add the fraction of women between the ages of 25 and 64 who are employed to our regression model.8 Columns 4-6 of Table 2 show how this addition changes the estimated effects of our labor market variables. The inclusion of the female employment variable has virtually no effect on the other estimated labor market coefficients prior to 1980 and very little effect on the estimates in the post-1980 period. At the same time, the female employment variable itself is strongly negatively correlated with the poverty rate in the later period (with no significant impacts in the earlier period). Using the coefficients in column 6, we again create counterfactual poverty rates for each year, this time using the female employment rate along with the labor market variables. This predicted poverty rate The potential for an individual's labor force participation to respond to the poverty rate means that the estimated coefficients in these columns may be contaminated by this reverse causality.
8
Povertyin America:Trendsand Explanations 59
Figure5 Actual and Predicted Nonelderly Poverty Rates, 1967-2003
0.25 -
Actual Predicted, full sample, all labor market variables Predicted, 1980-2003, all labor market variables Predicted, 1980-2003, all labor market variables and female employment
0.20 -
0.15rate 0.10 Poverty 0.05
0.00 1967
1972
1977
1982
1987
1992
1997
2002
Source:Authors's tabulations of the 1968-2004 March CPS. Note:Predictions are based on models 1, 3 and 6 in Table 2.
is shown by the dashed line in Figure 5. This exercise shows that the actual poverty rate is substantially higher than predicted by post-1980 labor market trends. This set of calculations brings a different conundrum to the surface. If median wage growth, rising inequality and the evolution of unemployment over the past 25 years do a good job of explaining changes in the poverty rate, then the rise in women's labor force participation suggests that poverty rates should have fallen by more than they did, conditional on the evolution of the other labor market variables. Of course, other factors may have also affected the poverty rate, including demographic changes in family structure, antipoverty spending and immigrationand we now turn to these factors.
Family Structure There have been tremendous changes in family structure and living arrangements over the past 35 years. Between 1967 and 2003, for example, the fraction of nonelderly individuals living in families headed by a single female doubled, from approximately 6 percent to 12 percent. Since the poverty rate among those in female-headed families is typically three or four times as high as in the overall population, such changes in the distribution of family types can have potentially large effects on poverty. Many authors have explored the extent to which demo-
60 Journal of EconomicPerspectives
Table3 Effect of Family Structure on Nonelderly Poverty Rates
Personsbyfamily type Married couples with children Married couples without children Single women with children Single men with children Single women without children Single men without children
Percentageof nonelderlypersonsby family type
Percentageof nonelderlypersonsin povertybyfamily type
1967
2003
1967
2003
67.3 18.7 6.2 0.8 4.4 2.6
44.2 22.4 11.9 2.5 9.6 9.3
10.7 5.8 51.2 28.4 25.4 18.1
8.1 4.1 37.3 22.0 18.6 16.2
13.3
12.8 17.0
All persons Percentage in poverty, actual Predicted poverty, changes in family type only Source:Authors' tabulations of the 1968 and 2004 March CPS.
graphic changes can explain trends in the poverty rate (Cancian and Reed, 2001; Blank and Card, 1993). Here we update that literature. Table 3 presents the results of our analysis. The first two columns of Table 3 show the distribution of individuals in 1967 and 2003, by family type. We categorize individuals by one of six different family types: married individuals with and without children; single females with and without children; and single males with and without children. Table 3 shows that in 2003, 67 percent of persons lived in married couple families, down from 86 percent in 1967. In contrast, the percentage of persons living in unmarried parent families increased from 7 percent in 1967 to 14.4 percent in 2003. In columns 3 and 4, we provide the actual poverty rates for persons in each family type. While poverty rates decreased between 1967 and 2003 for all groups, there are persistent differences across groups-with the highest poverty rates for persons in single parent families and the lowest poverty rates for persons in married couple families. We can use these data to illustrate the change in poverty between 1967 and 2003 that is predicted purely from changes over time in the fraction of individuals living in different family types. Specifically, we hold constant the poverty rates within each family type at their 1967 level, but allow the fraction of individuals living in each family type to change to their 2003 levels. Changes in family structure alone predict that poverty rates should have risen from 13.3 percent in 1967 to 17 percent in 2003. Thus, like the changes in unemployment, median wages and wage inequality, changes in family types substantially overpredict the actual increase in poverty rates over time. How were the higher poverty rates predicted by the population shift toward
HilaryW.Hoynes,MarianneE. Pageand Ann Huff Stevens 61
female-headed households avoided1 Cancian and Reed (2001) show that the increase in poverty was not as extreme as predicted by the changes in family structure, because this trend was accompanied by an increase in women's earnings and labor force attachment. Increases in women's education levels were another countervailing force.
Government Tax and Transfer Programs Government tax and transfer programs represent an important source of income for the poor. Among the nonelderly poor, the main sources for cash welfare benefits are provided through the Temporary Assistance to Needy Families program (formerly called Aid to Families with Dependent Children (AFDC)), General Assistance and the Earned Income Tax Credit (EITC). In addition to these cash-based assistance programs, programs like Food Stamps, Medicaid and housing assistance offer in-kind benefits. Because government transfers provide families with cash and other benefits, they can have a direct impact on income and poverty. They can also have an indirect effect, by changing individuals' behavior (Sawhill, 1988). While an extensive literature investigates the labor supply effects of government transfersparticularly the former AFDC program-the literature on the impact of these programs on poverty tends to focus on direct impacts.9 Since the behavioral responses predicted by economic theory are expected to lead to reductions in income as government transfers make it less attractive to earn income,10 estimates produced by these studies are likely an upper bound. Nevertheless, because of the structure of government benefits and the definition of poverty, even the direct effect of government transfers on official poverty rates-which we argue is an upper bound effect-is expected to be relatively small. First, consider the Temporary Assistance to Needy Families program (TANF), which provides cash benefits to low-income (primarily female-headed) families with children. Holding constant any behavioral response, TANF will increase the incomes of the poor. However, it is expected to have little effect on the poverty rate because TANF transfers are phased out at income levels significantly below the poverty line. In contrast, the EITC, a federal tax credit targeted to low-income working families with children, transfers income much higher in the income distribution, but because the official definition of poverty is based on pretax income, tax benefits provided through the EITC do not directly affect the poverty
9 Two exceptions are Neumark and Wascher (2000), who estimate the impacts of the EITC on poverty rates, and Schoeni and Blank (2000), who estimate the impact of welfare reform on poverty rates. Both papers measure the indirect/behavioral impact of the programs on poverty. 10For example, see Moffitt (1983, 1992). The exception is the EITC, which has been found to increase labor supply for single mothers (Hotz and Scholz, 2003; Eissa and Hoynes, forthcoming).
62
Journal of EconomicPerspectives
rate. Finally, government spending on in-kind transfers will not have a direct effect on the official poverty rate because these transfers are not counted in income for the purposes of measuring poverty. Rather, they are targeted on social goals like improving nutrition and increasing access to medical care (Burtless, 1995; Blank, 1997). Nevertheless, even if these programs do not have much impact on the official poverty rate, they have the potential to improve the well-being of the poor significantly. For example, at low earnings levels, the EITC provides a generous earnings subsidy: in 2005, a family with one child with earnings under 17,830 was eligible for a tax credit equal to 34 percent of earnings. For a family with two or more children with earnings under 111,000, the tax credit is equal to 40 percent of earnings. Moreover, this credit is refundable, so that even though families with low earnings owe little income tax, they can receive a check from the government. The credit is not fully phased out until the family's income exceeds 131,030 for families with one child. (The full phase-out occurs at 135,263 for a family with two or more children.) If income from the EITC were to be included in the official measure of poverty it might push a non-negligible number of families above the poverty line. Similarly, in-kind benefits represent a substantive fraction of government spending on the poor: in 2002, in-kind programs represented about 80 percent of the 1522 billion in federal and state spending on means-tested benefits (Burke, 2003). Table 4, which is based on special tabulations by the Census Bureau, provides some insight on how big these effects might be. We present poverty rates in 2003 under several different alternative definitions of income for two groups: all nonelderly and children. Because the definition of what is included in income is shifting across this table, the level of any particular poverty rate in the table is tricky to interpret. Our focus here is on how including various government benefits would change the estimated poverty rates. In particular, the table shows how this measure would change if EITC payments, cash transfers and noncash transfers were fully included. Beginning with line (b), when after-tax income (excluding the EITC) is used to calculate the poverty rate, it increases the poverty rate by more than a percentage point. This is expected, since including tax payments lowers after-tax income. Including tax credits from the EITC in the definition of income, however, reduces the fraction of individuals who are counted as poor. Overall, including the EITC as income lowers the poverty rate by 1.7 percentage points, from 13.9 to 12.2 percent. Because EITC eligibility is sharply limited for households without children, the effects of the EITC on poverty among children (shown in the last column of table 4) are substantially larger-a reduction of 3.1 percentage points from 19.1 to 16 percent. Means-tested cash transfers have a smaller impact on the poverty rate because, as discussed above, the transfers occur at income levels that are substantially below the poverty line. Such transfers reduce the nonelderly poverty rate by 0.8 percentage points-from 12.2 to 11.4 percent. Non-means-tested cash transfers such as Social Security, unemployment compensation and worker's compensation actually
Povertyin America:Trendsand Explanations 63
Table4 Percentage of Persons in Poverty by Alternative Definition of Income, 2003, Measuring Impacts of Government Programs
(a) Official poverty measure (Money income = pretax, postgovernment cash transfers) Povertyreductiondue to EITC (b) Money income (official measure) less all taxes except EITC (c) Money income less all taxes (including EITC) Povertyreductiondue to means-testedcash transfers (d) Full income less taxes less means tested government cash transfersa (e) Full income less taxes Povertyreductiondue to non means-testedcash transfers (f) Pregovernment transfer money income less taxesb (g) Pregovernment transfer money income less taxes plus nonmeans tested cash government transfers Povertyreductiondue to means-testednoncash transfers (h) Full income less taxes (definition e above) (i) Full income less taxes plus Medicaid (j) Full income less taxes plus Medicaid plus other means-tested government noncash transfers
Nonelderly persons
Children
12.7
17.6
13.9 12.2
19.1 16.0
12.2 11.4
15.8 14.9
15.2 12.4
17.8 15.9
11.4 10.8 9.9
14.9 13.8 12.3
Source:U.S. Bureau of the Census (2005) and special tabulations by the Census Bureau. Notes:To locate these figures in the Census report, note that (a) is Census definition 1; (b) is Census definition la; (c) is Census definition ib; (d) is Census definition 11; (e) is Census definition 12; (f) is Census definition 8; (g) is Census definition 9; (i) is Census definition 13; and (j) is Census definition 14. Taxes include payroll taxes, federal and state taxes. Means-tested government cash transfers include TANF, Supplemental Security Income, means tested Veteran's payments and other public assistance. Non-means-tested government cash transfers includes Social Security, unemployment compensation, worker's compensation, nonmeans tested Veteran's payments, Railroad Retirement, Black Lung payments, Pell Grants and other educational assistance. Means-tested noncash transfers include food stamps, rent subsidies, and free and reduced-price school lunches. For details on simulating taxes, see O'Hara (2004). For details on calculating the value of noncash benefits, see U.S. Bureau of the Census (1992). a Full income includes pretransfer money income less means tested transfers plus capital gains, employer paid health insurance, Medicare and regular-price school lunches. b Income measure also includes capital gains and employer paid health insurance.
have a larger effect than means-tested cash payments, reducing the poverty rate by nearly 3 percentage points from 15.2 to 12.4 percent. The Bureau of the Census also provides calculations of income and poverty that include noncash transfers, which are based on assumptions about the cash equivalent value of each in-kind benefit program. The impacts on poverty are shown in lines (h), (i) and (j) of Table 4. Comparing lines (h) and (j), we see that means-tested noncash transfers reduce poverty by about 1.5 percentage points. Taken together, these calculations suggest that government programs do have a modest effect on poverty, even though many of them are not accounted for in the official rate. More to the point, these programs may have a substantial effect on the
64
Journal of EconomicPerspectives
poverty gap, the sum of the differences between income and the poverty line for all families below the poverty line. Scholz and Levine (2001) estimate that in 1997 taxes and transfers reduced this gap by 72 percent for all persons (that is, not just nonelderly persons). Further, TANF alone reduces the poverty gap by 5 percent, and all means-tested cash and noncash benefits reduce the poverty gap by 55 percent. It is important to remember, however, the estimates in Table 4 do not account for any behavioral effects induced by these programs. The EITC may reduce poverty more than it appears because by subsidizing earnings, it provides a greater incentive to work. On the other hand, cash and noncash means-tested transfers may reduce poverty rates by less than the already small estimates above because the high benefit-reduction rates as people earn additional income discourage work. Can trends in these government programs over time explain trends in poverty rates1 Spending on government programs has varied over time, and (for some programs) across states. Following our analysis of labor market opportunities above, we used the March Current Population Survey to construct the same variables at the state level for 1977-2003, along with several different measures of the generosity of government programs. We then ran regressions of the poverty rate on these different measures of government spending, including both state and year fixed effects. Not surprisingly given the relatively small effects of the programs themselves on poverty, we also find that changesin government spending over time explain very little of the trends in poverty rates (Hoynes, Page and Stevens, 2005).
Immigration Since 1980, the fraction of the population who are immigrants has doubled. On average, recent immigrants are less educated and have fewer skills than natives, so a higher fraction of them are poor. Table 5 shows that while 12.4 percent of natives had incomes below the poverty line in 1999, 17.4 percent of foreign born U.S. residents were living in poverty. These differences, combined with the rapid influx of immigrants in recent years, have led some to suggest that immigration is responsible for the fact that the poverty rate has not declined more dramatically over time. To evaluate this claim, we divide the population into two mutually exclusive groups-those who live in families headed by an individual who was born in the United States and those who live in families headed by an individual who was born abroad. We use data from the Integrated Public Use Microdata Series (Census) rather than the Current Population Survey, because the CPS does not include information on country of birth prior to 1993. Table 5 shows that between 1959 and 1999, the poverty rate among U.S. natives fell by almost 50 percent, from 20.6 percent to 12.4 percent, whereas poverty among the foreign born increased by 3 percentage points. The year 1959 is probably a poor starting point, however, since
Hilary W. Hoynes,MarianneE. Page and Ann Huff Stevens 65
Table5 Nonelderly Poverty Rates in Native and Immigrant Households, by Year
All persons
1959 1969 1979 1989 1999
Personsin householdsheaded by a native
Personsin householdsheaded by an immigrant
Povertyrate
Povertyrate
Percentageof population
Povertyrate
Percentageof population
20.6 12.4 12.3 12.9 12.4
20.9 12.5 12.1 12.5 11.8
95.8 95.9 94.0 91.4 87.9
14.1 11.2 15.6 17.5 17.4
4.2 4.1 6.0 8.6 12.1
Source:Authors' tabulations of 1960, 1970, 1980, 1990 and 2000 Census files.
the poverty rate fell so much between 1959 and 1969, while a growing and increasingly low-income immigrant population cannot explain much of the trend in poverty prior to 1980. On the other hand, if we focus on the second half of the period, we see that while poverty rates among natives have changed little, poverty rates among immigrants have increased by nearly two percentage points, and the fraction of the population that is foreign born has increased by six percentage points. Taken together, these changes should put upward pressure on the poverty rate, but how much1 To answer this question, we begin by considering the extent to which overall poverty would have declined if the share of immigrants had increased over time but immigrants and natives had kept same poverty rates as in 1979. We find that if the level of poverty among immigrants had stayed the same as it was in 1979, the rising share of immigrants would have increased the poverty rate from 12.3 percent (1979) to 12.5 percent (1999), a number that is only slightly bigger than the actual value of 12.4 percent. We also consider the effects of changes over time in the fraction of immigrants who are poor. If we hold population shares and native poverty rates constant at their 1979 levels, but allow poverty rates among immigrants to vary across Census years, then the predicted overall poverty rate in 1999 is about 0.1 percentage points higher than its 1979 level. Although recent immigrants are poorer than their predecessors, their fraction of the population is simply too small to affect the overall poverty rate by much. These calculations are based on an important assumption, however, which is that large influxes of immigrants do not reduce job opportunities available to natives. If the presence of immigrant workers depresses native's wages, then the overall impact of immigration on the poverty rate will be higher. Evidence on the labor market effects of immigration is mixed (see Borjas, 1999, for an overview of this literature), but it seems safest to consider these estimates as lower bounds.
66
Journal of EconomicPerspectives
Conclusions Despite robust growth in real GDP per capita in the last three decades, U.S. poverty rates have changed very little. A number of studies have suggested that the lack of improvement in the poverty rate reflects a weakened relationship between poverty and the macroeconomy. We find that this relationship has weakened over time, but in spite of this, changes in labor market opportunities-measured by median wages, unemployment rates and inequality-predict changes in the poverty rate rather well. Importantly, we find that the lack of improvement in poverty rates despite rising living conditions is due to the stagnant growth in median wages and increasing inequality. Holding all else equal, changes in female labor supply should have reduced poverty further, but an increase in the rate of female heads of families may have worked in the opposite direction. Other factors that are often cited as having important effects on the poverty rate do not appear to play an important role: these include changes in the number and composition of immigrants and changes in the generosity of antipoverty programs. Several issues remain for future work. First, what is causing the weakening of the relationship between GDP growth and wages at the lower end of the distribution1 Our analysis provides another motivation for understanding the change in this relationship. Second, what are the relationships among women's labor force participation, female headship, labor market opportunities for women and poverty rates1 Many analyses have linked two or three of these factors, but there may be important interactions among all of these that help determine the evolution of poverty rates. A related question is why rising women's labor force participation prior to 1980 did not push down poverty rates. Third, one might explore indirect mechanisms through which poverty rates may be influenced, like the possible behavioral responses of family structure choices to changing labor market opportunities or the possible influence of immigration on native's labor market opportunities. Finally, what explains the change in the responsiveness of poverty to macroeconomic indicators starting in the 1980s1 We show that it is not a simple matter of controlling more fully for wage growth, inequality and female employment; even after conditioning on these factors, we see changes in the effects of key determinants of the poverty rate after 1980. Labor market measures play an important role in determining overall poverty rates, but their role has changed over time, and they are likely to interact in important ways with demographic and other social changes. m We thank Alan Barreca,Melanie Guidi and PeterHuckfeldtfor excellentresearchassistance;Joseph Dalaker of the Census Bureau for useful conversationsand unpublished tabulations;and James Hines, TimothyTaylorand Michael Waldmanof thejournal for helpfuleditorialsuggestions.
Povertyin America:Trendsand Explanations 67
References Bane, MaryJo and David Ellwood. 1986. "Slipping Into and Out of Poverty."Journal of Human Resources.21:1, pp. 1-23. Bartik, Timothy. 1994. "The Effects of Metropolitan Job Growth on the Size Distribution of Family Income." Journal of RegionalScience.34:4,
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Blanchflower, David G. and Andrew J. Oswald. 1994. The Wage Curve.Cambridge, Mass.: MIT Press. Blank, Rebecca. 1993. "Why Were Poverty Rates So High in the 1980s1" in Povertyand Prosperityin the Late TwentiethCentury.Dimitri B. Papadimitriou and Edward N. Wolff, eds. London: Macmillan Press, pp. 21-55. Blank, Rebecca. 1997. It Takes a Nation. Princeton, N.J.: Princeton University Press. Blank, Rebecca and David Card. 1993. "Poverty, Income Distribution and Growth: Are They Still Related1" BrookingsPaperson EconomicActiv-
ity.2, pp. 285-339. Borjas, George J. 1999. "The Economic Analysis of Immigration," in Handbookof LaborEconomics. Orley Ashenfelter and David Card, eds. Amsterdam: Elsevier Science, pp. 1698-757. Burke, Vee. 2003. Cashand NoncashBenefitsfor Personswith LimitedIncome:EligibilityRules, Recipient and ExpenditureData, Fiscal Years2000-2002. Washington, D.C.: Congressional Research Service. Burtless, Gary. 1995. "Public Spending on the Poor: Historical Trends and Economic Limits," in Confronting Poverty:Prescriptionsfor Change. Sheldon Danziger and Daniel Weinberg, eds. Cambridge, Mass.: Harvard University Press,
pp. 51-84.
Cancian, Maria and Deborah Reed. 2001. "Changes in Family Structure," in Understanding Poverty.S. Danziger and R. Haveman, eds. New York: Russell Sage Foundation, pp. 69-96. Citro, Constance and Robert Michael. 1995. MeasuringPoverty:A New Approach.Washington, D.C.: National Academy Press. Danziger, Sheldon and Peter Gottschalk. 1995. America Unequal. Cambridge, Mass., and New York: Harvard University Press and Russell Sage Press. Danziger, Sheldon and Peter Gottschalk. 2004. "Diverging Fortunes: Trends in Poverty and Inequality." The American People: Census 2002, Population Reference Bureau Bulletin. Economic Report of the President. 2005. Eissa, Nada and Hilary Hoynes. Forthcoming.
"Behavioral Responses to Taxes: Lessons from the EITC and Labor Supply." Tax Policy and the Economy. Fisher, Gordon. 1992. "The Development and History of the Poverty Thresholds." SocialSecurity Bulletin. 55:4, pp. 3-14. Engelhardt, Gary and Jonathan Gruber. 2004. "Social Security and the Evolution of Elderly Poverty." NBER Working Paper No. 10466. Freeman, Richard. 2001. "The Rising Tide Lifts... 1" in UnderstandingPoverty.S. Danziger and R. Haveman, eds. Russell Sage Foundation: New York, pp. 97-126. Gottschalk, Peter. 1997. "Inequality, Income Growth and Mobility: The Basic Facts."Journal of EconomicPerspectives.Spring, 11:2, pp. 21-40. Hines, James, Hilary Hoynes and Alan Krueger. 2001. "Another Look at Whether a Rising Tide Lifts all Boats," in The RoaringNineties: Can Full EmploymentBe Sustained.Alan Krueger and Robert Solow, eds. Russell Sage Foundation: New York, pp. 493-537. Hines, James, Hilary Hoynes and Alan Krueger. 2005. "WhatDid the Rising Tide Lift at the Turn of the Millennium1" Mimeo, University of California Davis. Hotz, V. Joseph and John Karl Scholz. 2003. "The Earned Income Tax Credit," in MeansTested TransferProgramsin the United States. R. Moffitt, ed. University of Chicago Press, Chicago, pp. 141-98. Hoynes, Hilary W. 2000. "The Employment and Earnings of Less Skilled Workers Over the Business Cycle," in FindingJobs: Workand Welfare Reform.Rebecca Blank and David Card, eds. New York: Russell Sage Foundation, pp. 23-71. Hoynes, Hilary W., Marianne Page and Ann Stevens. 2005. "Povertyin America: Trends and Explanations." NBER Working Paper No. 11681. Moffitt, Robert. 1983. "An Economic Model of Welfare Stigma." American EconomicReview. 73:5, pp. 1023-035. Moffitt, Robert. 1992. "Incentive Effects of the U.S. Welfare System: A Review."Journal of EconomicLiterature.30:1, pp. 1-61. Neumark, David and William Wascher. 2000. "Using the EITC to Help Poor Families: New Evidence and a Comparison with the Minimum Wage." NBER Working Paper No. 7599. O'Hara, Amy. 2004. "New Methods for Simulating CPS Taxes." Mimeo, U.S. Census.
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Sawhill, Isabel. 1988. "Poverty in the U.S.: Why is it so Persistent1"Journal of EconomicLiterature. September, 26, pp. 1073-086. Schoeni, Robert and Rebecca Blank. 2000. "What has Welfare Reform Accomplished1 Impacts on Welfare Participation, Employment, Income, Poverty, and Family Structure." NBER Working Paper No. 7627. Scholz, John Karl and Kara Levine. 2001. "The Evolution of Income Support Policies in Recent Decades," in UnderstandingPoverty.S. Danziger and R. Haveman, eds. New York: Russell Sage Foundation, pp. 193-228. Short, Kathleen, Thesia Garner, David Johnson and Patricia Doyle. 1999. "Experimental Poverty Measures: 1990-1997." U.S. Census Bu-
reau, Current Population Reports, Consumer Income, P60-205. Stevens, Ann Huff. 1999. "Climbing Out of Poverty, Falling Back In: Measuring the Persistence of Poverty Over Multiple Spells."Journal of Human Resources.34:3, pp. 557-88. U.S. Bureau of the Census. 1992. "Measuring the Effect of Benefits and Taxes on Income and Poverty: 1992." Current Population Reports, P60-186RD. U.S. Bureau of the Census. 2004. Statistical Abstract of the United States: 2004. Washington, D.C.: U.S. Bureau of the Census. U.S. Bureau of the Census. 2005. "Alternative Poverty Estimates in the United States: 2003." Current Population Reports, P60-227.
Attachment B The Paradox of Poverty in America
Page 89 of 102
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poverty is correlated with several factors. One that is clearly
Coast (Map 5).
significantly higher for children of color compared with white
residents lived below the poverty line, 33.2% of nonmetro
confined to rural areas. In 1959, while 18.3% of central city
In the first half of the 20th century, poverty was primarily
The Geography of Poverty
one third of Hispanic children also were poor (Table 5).
had high rates of poverty—22.3% in the early 1970s. Almost
poverty statistics for Hispanic families. Hispanic families, too,
It was not until 1972 that the federal government published
Figure 9. Distribution of poor by metro/nonmetro status, 1959
one in three chance of living below the poverty line; for mem-
For whites it was one in ten. Female-headed households had a
A person of color still had a one in four chance of being poor.
likely than a white person to be living below the poverty line. �
sistence. In 2003, as in 1959, a person of color was far more �
Another distinct quality of poverty in the nation is its per-
2003).
(1959 adjusted family income was $60,670 versus $43,318 in
important is the stagnation of real median family incomes
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10). The lack of decline in the absolute number of persons in
dian reservations, the Upper Peninsula, and the Atlantic Sea
family was headed by a male or female (Table 4). Rates were
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poverty has changed remarkably little in four decades (Figure
Appalachia, the Mississippi Delta, the U.S.-Mexico border, In-
lived below the poverty line in 1959 regardless of whether the
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over the last forty years, the absolute number of people in
(22%). Looking back to 1960, the poor were concentrated in
households lived in poverty. More than one in four children
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Despite a four-fold nominal increase in median family income
share (15%), followed by the Northeast (17%) and the Midwest
below the poverty line. Two thirds of black female-headed
children (39.6% versus 11.0%).
Poverty Forty Years Later: What Progress Has Been Made?
(Map 4). The relatively unsettled West had the lowest poverty
were poor. One out of two female-headed households lived
Map 5. The geography of poverty in 1960
South—the location of 46% of the nation’s poverty population
families lived below the poverty line, 54.9% of black families
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show that in the 1960s, the poor were concentrated in the
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of families lived in poverty (Table 3). While 16.5% of white
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Map 4. Distribution of the poor by region, 1969
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residents were classified as poor (Figure 9). Regional data
Table 5. Almost one third of Hispanic families with children were poor in 1972
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Table 3. One fifth of the nation was poor in the late 1950s
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In 1959, according to federal poverty statistics, at least 20.8%
The Demography of Poverty: Starting in the 1950s
insecurity, and income inequality on a daily basis.
portunity, many of its citizens experience poverty, economic
envisioned itself being four decades ago. In the land of op-
tell the story of America today. America is not the nation it
opportunity shared by all. It is a reflection we begin with to
1950s America reveals a nation poised to embrace a vision of
who live at or close to the poverty line. A reflection on late
by women, and a significant number of the nation’s elderly,
blacks and Hispanics), working families and families headed
with less than a college education, people of color (especially
Individuals and families at greatest risk for poverty are men
and prosperity in the nation has become more unequal.
decline, over the last twenty years the distribution of wealth
developed world. While income inequality was once on the
among the richest and the most economically insecure in the
American society is based on paradoxes. Its citizens are at once
Introduction: The Paradox of Poverty in America
��������������������������
1
Attachment C The Poverty and Inequality Report
Page 90 of 102
The Poverty and Inequality Report 2014
The Stanford Center on Poverty and Inequality David B. Grusky, Director Charles Varner, Associate Director Marybeth Mattingly, Research Director Michelle Poulin, Director of Publications Alice Chou, Administrator
The CPI, which is part of the Institute for Research in the Social Sciences, is generously supported by the Elfenworks Foundation and Stanford University. Partial funding for this research came from a grant to the Stanford Center on Poverty and Inequality from The Russell Sage Foundation. The United Way of the Bay Area also generously supported this initiative. The Stanford Center on Poverty and Inequality is funded by Grant Number AE00101 from the U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, awarded by the Substance Abuse Mental Health Service Administration. The contents of this report are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Health and Human Services (Office of the Assistant Secretary for Planning and Evaluation) or the Substance Abuse Mental Health Service Administration.
national report card â&#x20AC;˘ The Stanford Center on Poverty and Inequality
table of contents 4 Executive Summary 9 Roster of Experts 10 Labor Markets Michael Hout and Erin Cumberworth 15 Poverty Sheldon Danziger and Christopher Wimer 21 Safety Net Karen Jusko and Katherine Weisshaar 30 Income Inequality Jeffrey Thompson and Timothy Smeeding 36 Wealth Inequality Edward N. Wolff 44 Health Inequality Sarah Burgard and Molly King 53 Education Sean Reardon
national report card â&#x20AC;˘ The Stanford Center on Poverty and Inequality
4
executive summary
executive summary
the poverty and inequality report The Stanford Center on Poverty and Inequality
The Stanford Center on Poverty and Inequality (CPI), one of the countryâ&#x20AC;&#x2122;s three federally-funded poverty centers, is a nonpartisan organization dedicated to monitoring trends in poverty and inequality, examining what is driving those trends, and developing science-based policy on poverty and inequality. We present here our first annual report documenting trends across seven key domains and evaluating how the country is faring in its efforts to reduce poverty and inequality and equalize opportunity. The purpose of establishing this annual series of reports is to ensure that the key facts on poverty and inequality enjoy the same visibility as other indicators of the countryâ&#x20AC;&#x2122;s health. As it stands, there are all manner of analyses that focus on particular aspects of poverty and inequality, including excellent studies that take on separately such issues as employment, income inequality, wealth inequality, health inequality, or educational access. This report instead provides a unified analysis that brings together evidence across seven key domains, thereby allowing a global assessment of where problems exist, where achievements are evident, and how a coordinated effort to reduce poverty and equalize opportunity might be undertaken. In future years, we plan to expand the domains that we cover, and we also hope that many states and cities will join in this annual assessment of how we are faring on core poverty and inequality indicators.
national report card â&#x20AC;˘ The Stanford Center on Poverty and Inequality
executive summary
Methodology For each domain, top experts in the country have been asked to report on current conditions, the objective being to crisply characterize the best and most current evidence available. As a summary of their results, Table 1 presents some of the indicators relevant to the analyses, with a more detailed description of sources and definitions provided in the individual chapters. The rankings in Table 1 allow us to assess how each indicator stacks up across the 13 years since 2000 (with a ranking of 13th meaning that the current year is the very worst over this period).1 The Big Picture What, then, are the main conclusions of this report? It is difficult not to be struck by the sheer number of indicators in Table 1 for which the current year is one of very worst over the period we have covered. If an overall assessment is to be had, it is that the country’s economy and labor market remain in deep disrepair, whereas our various post-market institutions (e.g., the safety net, educational institutions, health institutions) have a mixed record of coping with the rising poverty and inequality that has been handed to them by a still-struggling economy and labor market. The latter conclusion holds across a variety of indicators. For example, we will show that the economy continues to fall well short of providing enough jobs, whereas the safety net has “stepped up” by supplementing at least some of the foregone earnings and raising many above the poverty threshold. Although the safety net thus deserves credit for responding well to the jobs disaster, it still falls short of meeting all the rising need. It therefore deserves a mixed grade insofar as it is held to the very stringent standard of fully addressing the need that is generated even during times of profound economic distress. The same characterization holds for the other post-market institutions that are covered in this report. As with the safety net, we again ask our health and educational institutions to perform rather the miracle, confronting as they do a population with high levels of poverty and inequality and all the health and educational problems that are thereby generated. This challenge has been met with only partial success. If one holds our health and educational institutions to the same high standard of fully rectifying the damage that the economy has wrought, then our report shows that they have fallen somewhat short and that much work remains to be done.
5
Key Findings This simple theme, that of a failing economy and struggling post-market institutions, plays out across many of the domains examined here. Although we will review some of the relevant results, we of course encourage readers to explore the far richer display of evidence within each chapter. A failing labor market
• In November 2013, six years after the start of the Great Recession, the proportion of all 25-54 year olds who hold jobs (i.e., “prime age employment”) was almost five percent lower than it was in December 2007, both for men and women alike. The ratio for men, currently at 82.7, is the 10th worst ratio over the last 13 years, while the ratio for women, currently at 69.2, is the 12th worst ratio over the last 13 years. • The long-term unemployment rate for men and women alike is near the all-time high for the period since 2000. Implication: Although the Great Recession ended over four years ago, the economy is still not delivering enough jobs. In the past, recoveries have not produced substantial employment gains beyond the sixtieth month after the recession began, a result that suggests that full recovery from the latest recession will likely not occur absent major labor market reform and intervention. Rising poverty
• The official poverty rate increased from 12.5 percent in 2007 to 15.0 percent in 2012, and the child poverty rate increased from 18.0 percent in 2007 to 21.8 percent in 2012. The current poverty rates for the full population and for children rank among the very worst over the 13 years since 2000 (i.e., both are ranked 11th). he latter increases in poverty, although substantial, •T would have been yet larger had the effects of the labor market downturn not been countered with aggressive safety net programs. Absent any safety net benefits in 2012, the supplemental poverty measure would have been 14.5 percentage points higher. Implication: In the recessions of the early 1980s and early 1990s, the poverty rate was also approximately 15 percent, even though these were more moderate downturns. Although the latest recession was more extreme than these prior ones, the rise in poverty has nonetheless been partly held in check by a responsive safety net.
national report card • The Stanford Center on Poverty and Inequality
6
executive summary
A ramped-up safety net
• In 2012, safety net programs in the U.S. provided 32 percent of the support that low-income households needed to reach 150 percent of the official poverty line, a level of “poverty relief” that is the third highest in the 13 years since 2000 (and also the third-highest over the last quarter-century). This support level is only slightly lower than the all-time high of 36 percent reached in 2010 as the Great Recession ended. • The safety net is increasingly fashioned to incentivize market work. As the Earned Income Tax Credit expanded in the early 1990s, households that increased their market earnings were better protected from sharp declines in their safety net support, a reform that ramps up the incentive to pursue market earnings. This rate of “relief falloff” has continued to grow gradually smaller up to the present day. As a result, our safety net is now better fashioned to incentivize market work, which is precisely the type of safety net that many people want. Implication: The safety net responded reasonably well to the challenges of the Great Recession. It delivered substantial poverty relief during the Great Recession because (a) a recessionary labor market generates precisely the type of need (e.g., unemployment) that our safety net is relatively well equipped to handle, and (b) the safety net was also modified in ways that responded well to the particular demands of this recession (e.g., extended unemployment benefits). Rising income inequality
• The Great Recession increased the amount of income inequality, but not the amount of consumption inequality or the share of total income going to the top one percent. • After the Great Recession ended in mid-2009, income and consumption inequality increased, thus resuming what has been a nearly relentless growth in inequality over the last 30 years. The lowest income quintile secured only 3.4 percent of total income in 2012. In the 1990s, it appeared as if the long-standing decline in the lowest quintile’s share had been staunched, but that downward march has now resumed. Implication: The equalizing effects of tax and transfer policy had a mild compressive effect on some forms of inequality in the Great Recession, but the longer-term trend towards growing inequality has resumed as more ambitious tax and transfer policies are relaxed. Likewise, the financial crisis had
an initial compressive effect (by reducing returns on assets that were disproportionately held by the advantaged), but that effect dissipated as capital markets recovered after the crisis. Rising wealth inequality
• Wealth inequality rose for the first time since the early 1980s. The Gini coefficient for 2010, the latest available year, is higher than any level recorded in nearly three decades. •T he Great Recession reduced the net worth of blacks and Hispanics much more than it reduced the net worth of whites. Implication: The decline in house values during the Great Recession increased wealth inequality because houses are the main asset of less advantaged groups. Although there are some new “safety net” programs oriented toward rectifying such losses in wealth (e.g., the Home Affordable Modification Program), these programs evidently did less compressive work than those programs offsetting declines in market income (e.g., extended unemployment insurance). It follows that wealth inequality, unlike income inequality, was not well held in check by our post-market response. A mixed record on health inequality
• Although there is improvement in some key health indicators, there is moderate deterioration in others. For example, 9.8 percent of Americans reported that they were in poor or fair health in 2012, an increase of 0.6 percentage points since 1997. •E conomic, racial, and ethnic disparities in health outcomes are often substantial and are sometimes increasing. The proportion of Blacks and Hispanics, for example, who could not afford necessary care rose at a faster rate during the Great Recession than did the corresponding proportion for Whites. ince 2000, the proportion of Americans who have any •S health insurance coverage has declined (to 84.6 percent in 2012), although there has been a slight reversal in this decline since 2010. The proportion of children, however, who are insured has increased during this same period and is now at the highest level since 2000. Implication: The decline in some health outcomes likely reflects recent increases in the poverty rate and the characteristically poorer health outcomes of those in poverty. It remains an open question whether future increases in health insur-
national report card • The Stanford Center on Poverty and Inequality
executive summary ance coverage (under the Affordable Care Act) will reverse some of these trends. Because health outcomes are affected by many forces other than coverage alone, the sizable health disparities currently observed may be resistant to any dramatic change.
7
are blamed for failing to eliminate income or racial disparities; and our healthcare institutions are blamed for poor health among the poor. We accordingly propose all manner of narrow-gauge safety net reforms, narrow-gauge school reforms, and narrow-gauge health care reforms; and we imagine that, if only we could find the right such reforms, all would be well.
A mixed record on educational inequality
• The record on black-white educational inequality is mixed, with black-white disparities in academic achievement declining by approximately forty percent over the last four decades, while disparities in college completion have increased over the same period. • The record on economic inequality is less favorable. The income gap, measured as the difference in average test scores between children whose families are at the 90th and 10th percentiles of the family income distribution, grew by forty percent across cohorts born in the early 1970s and late 1990s (although there are also hints of a more recent narrowing of this gap). This income gap is already very large when children enter kindergarten and grows only modestly thereafter. Implication: Because income gaps are already well in place when children enter kindergarten, it is clear that out-of-school factors are implicated in their growth. The key open question is whether substantial headway in closing such gaps can nonetheless be made via school reform alone. A Second War on Poverty? The foregoing suggests a broadly deteriorating poverty and inequality landscape. As Table 1 summarizes, such deterioration is revealed across a host of key indicators, including prime-age employment, long-term unemployment, poverty, income inequality, wealth inequality, and even some forms of health inequality. The facts of the matter, when laid out so starkly, are quite overwhelming.
We should of course commit to getting our post-market institutions right, but that very same critical scrutiny might also be applied to our economic and labor market institutions. The results presented here reveal an economy that is failing to deliver the jobs, a failure that then generates much poverty, that exposes the safety net to demands well beyond its capacity to meet them, that produces too many children poorly prepared for school, and that places equally harsh demands on our healthcare, penal, and retirement systems. These are profound downstream costs that are challenging and costly to address in a piecemeal institution-specific fashion. Although we should continue to tinker with each of these institutions to better meet the challenges that an ailing economy generates, it is worth considering whether a noholds-barred commitment to job-delivering reform might be a more efficient and sustainable way forward. These are of course big and complicated questions. The current tendency, unfortunately, is to shirk them altogether and move directly to piecemeal discussions about piecemeal reform. If our second war on poverty is to be a real war founded on a real commitment to win it, it is important that we step back and ask just such big questions, no matter how daunting they may be. ■
Note
It is important to conclude by briefly discussing the choices that our country faces in addressing such rising poverty and inequality. Although one of our objectives is simply to document changes in poverty and inequality across a variety of domains, another is to ask whether the pattern of results tells us anything about how a second War on Poverty, were we to choose to wage one, might have the greatest chance of bringing about meaningful and permanent change. The distinctively American approach is to blame our postmarket institutions for the current state of affairs. The safety net is blamed for failing to make a dent in poverty; our schools
1. For the labor market indicators, we have data extending into 2013. We have averaged values for 2012 and 2013 for this domain alone to make the number of observations (13) the same across domains and hence the rankings more nearly comparable. Also, the wealth inequality indicators only go up to 2010, thus for this domain a rank of 11th is the worst possible. In cases where there are ties across two or more years, our ranking algorithm assigns the best rank to the earliest year. We thank all of our contributors for sharing their data and especially thank Liana Fox, Irwin Garfinkel, Neeraj Kaushal, Jane Waldfogel, and Christopher Wimer for sharing their historical Supplementary Poverty series (see “Waging War on Poverty: Historical Trends in Poverty Using the Supplemental Poverty Measure,” 2013, CPRC Working Paper 13-02, http://cupop.columbia.edu/publications/2013). For methodological details on the measures, please consult the relevant domain reports.
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executive summary table 1.
Selected List of Poverty and Inequality Indicators by Domain
Domain
Type of Measure
Poverty
Official Poverty Rate
Supplemental Poverty Rate (Hist.) Labor Market
Official Unemployment Rate
Subpopulation
Most Recent Value
Full population
15.0
11
Children
21.8
11
Black non-Hispanic
27.0
11
Hispanic
25.6
12
Full population
16.0
12
Children
18.7
12
Full population
7.8
10
Men
8.0
10
Women
7.5
10
Black Hispanic Full population
8.3
10
9.2
10
All Underutilization (U-6 Rate)
Full population
14.3
10
Men 25-54
82.7
10
Women 25-54
69.2
12
Black men 25-54
70.7
10
Hispanic men 25-54
83.6
10
Men
42.5
11
Women
41.5
11
Black
45.1
11
Hispanic
36.6
11
Poverty Relief Ratio
Full Population
0.32
3
Baseline Relief
Full Population
3.77
3
Relief Falloff
Full Population
-0.09
2
Household Income Share
Lowest Quintile
3.4
13
Gini Coefficient
Household Income
Second Quintile
9.0
13
0.44
12
Disposable Income
0.38
11
Consumption
0.29
10
Top 1 Percent Share
IRS
22.5
11
Gini Coefficient (to 2010 only)
Net Worth
0.87
11
Mean Net Worth (to 2010 only) Health Inequality
10
Full population
(as percent of unemployed)
Wealth Inequality
10
9.8
Discouraged Workers (U-4 Rate)
Long Term Unemployment
Income Inequality
13.5
Marginally Attached (U-5 Rate) Employment to Population Ratio
Safety Net
Rank
Poor or Fair Health
Black/White
0.14
11
Hispanic/White
0.15
11
Poor/Rich
5.39
12
Near Poor/Rich
4.07
12
Middle Class/Rich
2.36
13
Asthma (from 2001 only)
Black/White
2.03
10
Hispanic/White
1.11
10
Insurance Coverage
Full Population
0.85
10
Children
0.91
1
Delayed Care
Full Population
0.11
9
Foregone Care
Full Population
0.08
9
Black
0.12
9
Hispanic
0.11
9
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roster of experts Sarah Burgard, Associate Professor of Sociology and Epidemiology, University of Michigan Erin Cumberworth, National Poverty Fellow, Center on Poverty and Inequality, Stanford University Sheldon Danziger, President, Russell Sage Foundation, and University Professor of Public Policy, University of Michigan Michael Hout, Professor of Sociology, New York University Karen Jusko, Assistant Professor of Political Science, Stanford University Molly King, National Poverty Fellow, Center on Poverty and Inequality, Stanford University Sean Reardon, Professor of Education, Stanford University Timothy Smeeding, Director, Institute for Research on Poverty, and Arts and Sciences Distinguished Professor of Public Affairs, University of Wisconsin Jeffrey Thompson, Economist, Federal Reserve Board Katherine Weisshaar, National Poverty Fellow, Center on Poverty and Inequality, Stanford University Christopher Wimer, Research Scientist, Columbia Population Research Center, Columbia University Edward N. Wolff, Professor of Economics, New York University, and Research Associate, National Bureau of Economic Research
national report card • The Stanford Center on Poverty and Inequality
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labor markets
labor markets January 2014 The Stanford Center on Poverty and Inequality
By michael hout and Erin Cumberworth
Key findings • Men’s and women’s primeage employment declined more during and after the Great Recession than at any time since record keeping began in 1947 and shows only weak signs of recovery. In November 2013, six years after the start of the Great Recession, men’s and women’s prime-age employment ratios were almost five percent lower than they were in December 2007. • Although job loss affected most sectors of the American society, people who lacked educational credentials bore a disproportionate burden. Over the course of the recession, the prime-age employment ratio dropped 15 points for men without a high school diploma compared to 10 points for men with high school diplomas and just 5 points for men with college degrees. • Unemployment in industries that drove the recession, such as construction or financial services, rose from the onset of the recession until its end, but then almost fully recovered after the recession ended. “Bystander industries,” such as public administration, education and health care, have failed to recover, implying that the austerity in public spending is delaying recovery.
A
mericans work for their living. For most people, a job is both an economic and moral imperative. The wages they earn fuel the rest of the economy. Employment begets the spending that begets more employment. In good times, it is a virtuous cycle reinforcing consumer-driven capitalism. Events like the financial crisis of 2007 and 2008 can reverse the cycle, spinning the economy downward with a momentum that can be hard to break. Job losses reduce spending, which kills more jobs, reducing spending even more. The Great Recession of 2007 to 2009 played out these general principles of recession economics in every aspect, but with an uncommon intensity. The “housing bubble” burst, the financial sector tumbled, banks stopped lending, construction workers lost their jobs, sales of building materials and appliances plummeted, tax revenues fell, and the downward spiral threatened to spin ever lower. The government saved the banks and stimulus spending broke the fall in employment. But employment has barely kept pace with population growth since the recovery began in the summer of 2009. The U.S. economy enters 2014 with 7 percent of the labor force unemployed and millions more out of the labor force. In this brief, our aim is to assess the current standing of the U.S. labor market, a task that inevitably requires us to address the enduring effects of the Great Recession. We will put the Great Recession in historical context, looking both at its overall impact and at how the burdens were distributed across
the population by gender, level of education, and industry. Historical Context The single best index of employment is the prime-age employment ratio—the ratio of employed 25-54 year-olds to the population of that age. The more familiar unemployment rate gives a reasonably accurate picture of employment during good times, but during recessions many people who would prefer to be working will stop looking. The unemployment rate does not count them so it makes the economy look better than it is. As a recovery starts, those people reenter the labor market, making unemployment look worse until they find a job. The prime-age employment ratio overcomes this “discouraged worker” problem by keeping tabs of everyone whether they are looking for work or not. Figure 1 plots the prime-age employment ratio for men and women separately from the earliest to the most recent data, with recession months shaded gray. When the Great Recession began in December of 2007, 87.5 percent of American men 25-54 years old were employed; at the low point two years later, 80.4 percent were (a decline of 8.1 percent). The path upward from that low point has been very unsteady; by November of 2013, men’s prime-age employment ratio was still a very low 82.8 percent (5.0 percent below its level at the onset of the recession). Women’s employment declined more slowly but shows practically no sign of recovery. When the Great Recession began in December of 2007, 72.4 percent of prime-age
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labor markets
women were employed; women’s employment bottomed out in November 2011 at 68.7 percent (5.1 percent below its level at the onset of the recession) and it had increased by barely one-half of a percentage point to 69.4 percent by November of 2013. At the bottom of the recession, men’s prime-age employment was lower than at any time since the data were first collected in 1947; women’s employment was lower than at any time in the last twenty-five years. Men’s and women’s prime-age employment declined more during and after the Great Recession than at any time on record. For men, that record shows a net decline from a longago peak of 96 percent in 1953 to the most recent 83 percent. Each postwar recession reduced prime-age employment, and since the 1970s post-recession employment always fell short of its pre-recession high. Women’s employment increased so dramatically during the twentieth century that recessions more often slowed growth than reversed it. After the 2001 recession, however, women’s prime-age employment failed to rebound to its pre-recession level for the first time on record; it has happened again after the Great Recession as women’s most recent prime-age employment ratio is about where it was when the recession officially ended in the summer of 2009. The point estimate for November 2013 is one point lower than the point estimate for June 2009. Because the margin of error on each is 1.5 percentage points, we cannot say for sure that the ratio is lower now than then.
figure 1.
To learn more about the Great Recession and its aftermath, we align the prime-age employment ratios of three recessions by measuring time relative to the onset of the recession. We picked two recessions for our comparison: the doubledip recession of 1980-1982 and the recession of 2001. The 1980-1982 recession is interesting because until the Great Recession it was the most severe recession of the postwar era; it is useful to compare one strong recession with another. The 2001 recession is interesting because it was the first one in which women’s employment failed to recover to pre-recession levels; some commentators referred to the post-recession period as a “jobless recovery.” Figure 2 shows, for women and men separately, the change in prime-age employment relative to its level at the onset of recession plotted against months since the recession started (actually starting the time series six months prior to the onset of recession). We smoothed the time series to remove the distraction of short-term fluctuations best ascribed to statistical sampling error. Men’s prime-age employment fell almost 7 percent in the two years following the onset of the Great Recession, recovered two percentage points over the next two years, and changed little in the last two years. Women’s prime-age employment fell less but longer so that today, six years after the Great Recession began, men’s and women’s prime-age employment ratios are both almost five percent lower than they were in December 2007.
Prime-age Employment Ratio by Month and Gender, 1947-2013.
100 Men
ployment to Population Ratio
employment to population ratio (%)
85 Women 70
55
40
25 1950
1960
1970
1980
11
1990
2000
Source: Bureau of Labor Statistics Note: We used seasonally adjusted data for people who were 25 to 54 years old.
national report card • The Stanford Center on Poverty and Inequality
2010
12
labor markets
The 2001 recession lasted half as long and was much less severe than the Great Recession, but there were some similarities in the timing and gender patterns. Men’s prime-age employment fell for two years before rebounding but failing to reach its pre-recession level. Women’s employment fell slower but longer, and it too failed to recover to its pre-recession level. The double-dip recession of 1980-1982 lasted three years and raised the unemployment rate (not shown) to over 10 percent. Men’s prime-age employment fell throughout the recession but began to rebound almost immediately after the recession ended. Five years after the recession began, men’s employment was still almost two percent lower than it had been at the beginning in January 1980. Women’s employment was on a sharp upward path as the recession started. It slowed but did not fall during the first part of the recession, plateaued during the second, and then resumed its climb as soon as the recession ended. There are at least three reasons why conditions following the 1980-1982 recession differed from those in recent years. First, deregulation of the savings and loan industry sparked a housing bubble that dramatically increased employment in
the construction industry. When that bubble burst in 1990, many savings and loan banks failed and the economy went into recession, but its immediate impact was to put men (especially) to work building new housing. Second, personal computers became popular. Most were made in the United States, increasing employment in manufacturing. Third, Chrysler and other car makers started making minivans and sport utility vehicles that revived American automobile manufacturing. Nothing of that sort has emerged in recent years to stimulate employment growth. None of these recoveries (and none of the others we looked at but do not show) produced significant employment gains beyond the sixtieth month (i.e., five years) after the recession began. In the 1950s, 1960s, and 1970s, recessions were about five years apart. Since 1980, recessions have been less frequent, but no recovery has been sufficient to return prime-age employment to pre-recession levels. That strongly suggests that full recovery from the Great Recession will not occur unless and until the federal government enacts a second stimulus package. The political environment makes a stimulus highly unlikely, but the slack in the U.S. job market implies that the economy needs it.
figure 2. Change in Prime-age Employment Ratio by Gender and Months Since the Beginning of the Recession, 1980-1986, 2001-2007, and 2007-2013.
1980-82
2001
2007-09
change in prime-age employment ratio (%)
5.0
2.5
0.0
-2.5
-5.0
-7.5 0
12
24
36
48
60
72
0
12
24
36
48
60
72
0
12
24
36
48
60
72
months since recession started
Women
Men
Source: Authors’ calculations from seasonally adjusted data provided by the Bureau of Labor Statistics, 2013. Note: Time series smoothed to reduce the influence of statistical sampling error. Women’s employment rose linearly from 2.5 at the end of the recession in 1982 to 10.0 in month 72. To highlight other aspects of the data we truncated the women’s time series at 5.0 and indicated that it continued with dashes.
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labor markets Human Capital Accounts of the recession in the popular media frequently feature struggling college graduates. The data suggest that this storyline may not be totally without foundation, but it is misleading and overstated.
Prior to the recession, unemployment for people with less than a high school degree hovered around 7 percent, while unemployment for college graduates was only about 2 percent. As unemployment spread, the rate for each educational category rose more or less proportionally. At peak unemployment in 2010, the rate for people without a high school degree had increased from 7 to nearly 15 percent and the rate for college graduates had increased from 2 to about 4.7 percent. The baseline differences were so large that proportional increases raised unemployment most for the least-educated and least for the most-educated. Even though unemployment rose for everyone, people without a high school degree bore a much greater unemployment burden. Industry The Great Recession started with a financial crisis that pushed both banks and homeowners to the brink of insolvency. A federal bailout saved the banks and subsequent legislation helped some homeowners. But the immediate fallout was a credit crunch that reduced consumers’ ability to borrow money.
figure 3.
That, in turn, reduced the demand for manufactured goods. All of these changes affected employment. We should see the effects in data on employment in some industries more than others. For this analysis we switch from the prime-age employment ratio to the more conventional unemployment
Prime-age Employment Ratio by Month, Educational Attainment, and Gender, 2001-2013.
Men
Women
employment to population ratio (%)
100
80
60
40 2000
2004
Less than high school
2008
2012
2000
High school diploma
Some college
2004
2008
College degree
2012 Advanced degree
Source: Authors’ calculations from Bureau of Labor Statistics data, 2013. Note: Time series smoothed to reduce the influence of statistical sampling error.
figure 4.
Unemployment Rate by Month, Industry, and Gender, 2005-2013.
Men
Women
2005 2006 2007 2008 2009 2010 2011 2012 2013
2005 2006 2007 2008 2009 2010 2011 2012 2013
20
unemployment rate (%)
Figure 3 shows that prime-age employment is more likely among the better-educated—in good times and bad. The recession has amplified college graduates’ advantages, not eroded them. The need to take a lower-paying job may make paying back college loans harder, but at least college graduates are getting jobs. The jobs college graduates now get typically go to high school graduates in tighter labor markets. It is high school graduates and high school dropouts who have borne the brunt of the Great Recession.
13
15
10
5
0
Construction
Manufacturing
Financial services
Education & health care
Public administration
Source: Authors’ calculations from Bureau of Labor Statistics data, 2013. Note: Data restricted to persons 25-54 years old. Industries selected from a full set of 13. Time series smoothed to reduce the influence of statistical sampling error.
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labor markets
rate, though we do keep the age restriction and limit our attention to 25-54 year olds. Figure 4 shows the unemployment rates in five key industries from January 2005 to November 2013. The recession months are marked in gray. Again we smooth the data because the relatively small sample sizes in specific industries produce substantial statistical sampling error. Unemployment increased first in construction, manufacturing, and financial services—the three industries most affected by the financial crisis that precipitated the Great Recession. Construction workers typically live with spells of unemployment, so their unemployment rate was already 6.5 percent before the recession started. At its peak in the summer of 2010, the unemployment rate in construction was 15 percent for women and over 18 percent for men. Unemployment in manufacturing doubled for both women and men. Unemployment in financial services also rose from the onset of the recession until its end. Significantly, the unemployment rates in these three industries also started to decline almost as soon as the recession ended. The decline was faster for men than women, but the most recent data show that unemployment in all three of these most-affected industries is now only slightly higher than before the recession. Unemployment in public administration and in education and health care increased later than it did in the industries that were directly affected by the recession. But these two industries show no signs of recovery. Unemployment is significantly lower in these industries than in construction or manufacturing in each year, but the lack of any recovery-based trend
since 2010 is telling. What it tells is the tale of austerity in public spending. The recession dramatically reduced tax revenues. Governments did not respond instantly, but once they did their cutbacks raised unemployment in education and public administration. Conclusions The Great Recession was a jobs disaster that took unemployment to heights seen only once before in over fifty years—in 1982. In 2009 and 2010, the U.S. economy hit postwar highs in job loss, the portion of the labor force unable to find work, and the duration of unemployment spells. The Great Recession was the sixth recession since 1970. In all six post-recession recoveries, men’s prime-age employment was lower four years into recovery than when the recession started; in the last two, women’s prime-age employment was also below the pre-recession level. It is almost as if the economy recovers because of job losses not despite them. The latest employment data suggest that the consumer-driven private economy cannot spark an employment recovery on its own. Productivity increased, profits soared, and Wall Street recovered since 2009. But overall employment languishes at levels barely above recession lows. Americans value work and need to work. The private sector economy seems incapable of delivering on that goal. The public sector seems incapable of anything but austerity. History and logic caution that full employment will not return without a private-sector breakthrough or a public sector stimulus. ■
Additional Resources Jesse Rothstein, “The Labor Market Four Years into the Crisis: Assessing Structural Explanations.” NBER Working Paper No. 17966. Available at: http://www.nber.org/ papers/ w17966.
Holzer, Harry. 2013. “Good Workers for Good Jobs: Improving Education and Workforce Systems in the U.S.” University of Wisconsin Institute for Research on Poverty Discussion Paper No. 1404-13.
Gerson, Kathleen. 2011. The Unfinished Revolution: Coming of Age in a New Era of Gender, Work, and Family. Oxford U Press.
national report card • The Stanford Center on Poverty and Inequality
national report card
poverty
poverty
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January 2014 The Stanford Center on Poverty and Inequality
By Sheldon Danziger and Christopher Wimer1
• While the official poverty rate has declined from 22 percent to 15 percent since 1959, most of this progress occurred before the early 1970s. Since then, the direct connection between poverty and economic growth has weakened. • Some subgroups, like young adults and less-educated Americans, have fared worse than others, as poverty rates for these subgroups have risen over time. Others, such as the elderly, have fared much better than others. • The Official Poverty Measure masks important progress that has been made in fighting poverty because it doesn’t count many of the antipoverty programs that have accounted for an increasing share of all safety net benefits in recent years. • If the benefits from noncash programs like food stamps and the Earned Income Tax Credit are counted, the poverty rate would stand at about 11 percent today instead of 15. • Poverty remains high primarily because the economy has failed the poor. The expanded safety net has kept poverty from being even higher than it is today.
This apparent lack of progress against poverty cannot be blamed on the economic devastation wrought by the Great Recession, although that certainly increased poverty over the last five years. Rather, the direct connection between economic growth and figure 1.
poverty reduction is now much weaker than in the past. Poverty remains high because many workers have not shared in the economic gains of the past 40 years; instead most of those gains have been captured by the economic elite. Over these same decades, the official poverty measure has increasingly obscured some of the progress that has been made in reducing poverty because it fails to account for many government benefits the poor now receive, such as Food Stamps and the Earned Income Tax Credit. If these safety net benefits were counted as family income, today’s official poverty rate would fall from 15 to about 11 percent. The purpose of this research brief is to lay out where we now stand on the war on poverty. We first describe long-term trends in
Trends in Official Poverty
40 35 30 25 percent
Key findings
What has happened since President Lyndon Johnson declared an unconditional War on Poverty in his January 8, 1964 State of the Union Address? There is no doubt that the United States has become a more affluent nation since that famous declaration: Real gross domestic product (GDP) per capita has in fact doubled over the past 50 years. Despite this growth, the official poverty rate for 2012 now stands at 15 percent, a full 4 percentage points higher than it was during the early 1970s. And the poverty rate is only 4 percentage points lower than the 19 percent rate of 1964.
35.2
27.3 21.8
22.4
20 15
15.0
10 9.1
5 0 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2012 Percent Poor
Percent Poor, Children
Source: U.S. Census Bureau, Historical Poverty Tables
Percent Poor, Elderly
poverty
poverty for the full population and for key subpopulations; we next examine why poverty has remained stubbornly high; we then discuss more appropriate ways to measure poverty that reveal how the modern safety net significantly reduces poverty. We conclude by discussing trends in extreme poverty and deep poverty. The theme throughout is that labor market failures—not safety net failure—is a main reason why progress against poverty has been so difficult. Key Trends in Poverty Figure 1 shows trends in the official poverty rate for all persons, the elderly, and children. In 1959, 22.4 percent of all persons were poor according to the official measure. This was cut in half by 1973 because of rapid economic growth and the expansion of safety net programs in the aftermath of the War on Poverty.2 But nothing much happened for the next four decades. The poverty rate has never fallen below the historic low of 11.1 percent reached in 1973, and only in the booming economy of the late 1990s did it come close to that mark. Instead, the trend over the past 40 years consists of ups during recessions and downs during economic recoveries, but no long-term progress. Most disturbing, the child poverty rate in 2012, 21.8 percent, was as high as it was in the mid-1960s. Worse yet, some groups have experienced an increase in their poverty rates.3 We examine the official poverty rate for adults
percent poor
figure 2.
Poverty Rates for Nonelderly Adults by Age Cohort 1968-2012
classified by age cohort (Figure 2), educational attainment (Figure 3), and race or ethnicity (Figure 4). As shown in Figure 2, the poverty rate for 18-24 year olds increased by about 11 percentage points and the rate for 25-34 year olds by about 5 points since 1968.4 Figure 3 shows that adults without a college degree have fared badly, with the poverty rate for those without a high school degree increasing by almost 20 percentage points and the rate for high school graduates by about 10 points since 1968. Figure 4 shows that poverty rates for both Hispanics and White non-Hispanics are higher in 2012 than in 1970, while the rate for Black non-Hispanics is slightly lower.5 Cleary, the goals of the war on poverty have not been achieved. Although there have been many important successes (as will be discussed subsequently), much remains to be done. In the next section, we ask what went wrong as well as what went right, questions best addressed by taking an historical perspective. What Went Wrong? And What Went Right? To understand recent trends in poverty, we begin with the economic situation in the quarter century after the end of World War II. Rapid economic growth at that time translated into more employment, higher earnings, and increasing family incomes for most Americans. Poverty fell as the living standards of the poor and the middle class increased as rapidly as they did for the rich.
figure 3.
40
40
35
35
30
30
25
20.4
20 15
11.7 10.1
10 5
16.0 12.5
Poverty Rates by Educational Attainment, Persons Ages 25-64
33.9
25 20 15.4
15.6
15
10.9
10
8.3
7.6
percent poor
16
10.8 10.7
4.9
4.0
5 8.1
0 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 Age 18–24
Age 35–44
Age 25–34
Age 45–54
Age 55–64
Source: Stanford Center on Poverty and Inequality calculations using March CPS microdata downloaded from IPUMS (King et al., 2010).
4.5
3.0
0 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 Less than HS
Some College
HS Only
College
Source: Stanford Center on Poverty and Inequality calculations using March CPS microdata downloaded from IPUMS (King et al., 2010).
national report card • The Stanford Center on Poverty and Inequality
poverty
“Americans today enjoy the highest standard of living in the history of mankind. But for nearly a fifth of our fellow citizens, this is a hollow achievement. They often live without hope, below minimum standards of decency… . We cannot and need not wait for the gradual growth of the economy to lift this forgotten fifth of our nation above the poverty line. We know what must be done, and this Nation of abundance can surely afford to do it. …Today, as in the past, higher employment and speedier economic growth are the cornerstones of a concerted attack on poverty... .But general prosperity and growth leave untouched many of the roots of human poverty.” The Johnson administration proposed many strategies for reducing poverty. The 1964 Economic Report of the President argued for maintaining high levels of employment, accelerating economic growth, fighting discrimination, improving labor markets, expanding educational opportunities, improving health, and assisting the aged and disabled. Indeed, these remain important antipoverty priorities. This last goal, assisting the aged and disabled, is widely accepted as the greatest achievement of the War on Poverty. Elderly poverty has fallen dramatically, from 35.2 percent in 1959 to 9.1 percent in 2012 (see Figure 1). Medicare and Medicaid, introduced in 1965, greatly expanded access to medical care and improved the health of the elderly and disabled. An expanded safety net raised their incomes and insulated them from both recessions and inflation, through the expansion and indexation of social security benefits and the introduction of the Supplemental Security Income program. The poverty rate for the elderly has been lower than the rate for working-age adults for the past two decades. But the Johnson administration’s optimism that macroeconomic policies and an expanded social safety net could eliminate poverty for all persons had all but disappeared by the 1980s. Many observers focused on the limited progress that had been made in reducing poverty among the population as a whole. The War on Poverty programs came to be seen as the cause of the problem, to the point that in his 1988 State of the Union Address President Reagan declared:
“In 1964, the famous War on Poverty was declared. And a funny thing happened. Poverty, as measured by dependency, stopped shrinking and actually began to grow worse. I guess you could say “Poverty won the War.” Poverty won, in part, because instead of helping the poor, government programs ruptured the bonds holding poor families together.” Was President Reagan right? Are safety net programs to blame for the stagnation in the official poverty rate since the early 1970s? The short answer: No. A careful analysis reveals that the lack of progress results from two opposing forces —an economy that has increasingly left more of the poor behind and a safety net that has successfully kept more of them afloat. The primary reason that poverty remains high is that the benefits of economic growth are no longer shared by almost all workers, as they were in the quarter century after the end of World War II. In recent decades, it has been difficult for many workers, especially those with no more than a high school degree (see Figure 3), to earn enough to keep their families out of poverty. This economic trend represents a sharp break with the past. Inflation-adjusted median earnings of full-time year-round male workers grew 42 percent from 1960 to 1973. But, four decades later, median earnings were $49,398 in 2012, four percent lower than the inflation-adjusted 1973 value,
figure 4.
Poverty Rates by Race/Ethnicity Persons Ages 18-64
40 35 30.8
30 percent poor
Yet, many families were being left behind during this period of rapid growth, as careful observers such as Michael Harrington, John Kenneth Galbraith, and Robert Lampman pointed out. The paradox of “poverty amidst plenty” led President Johnson to declare “unconditional” war on poverty in his first State of the Union address on January 8, 1964. He emphasized that the fight against poverty could not rely solely on economic growth:
17
25
27.0 25.6
22.8
20 15 10
7.5
9.7
5 0 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 Black Non-Hispanic
Hispanic
White Non-Hispanic
Source: http://www.census.gov/hhes/www/poverty/data/historical/people.html and Stanford Center on Poverty and Inequality calculations using March CPS microdata downloaded from IPUMS (King et al., 2010).
national report card • The Stanford Center on Poverty and Inequality
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poverty
$51,670.6 Men with no more than a high school degree fared even worse. Further, men are less likely to be working today than in the past. The annual unemployment rate for men over the age of 20 was below 5 percent in 92 percent of the years between 1950 and 1974, but in only 37 percent of the years since (see the Labor Market brief for more details). Stagnant earnings for the typical worker and higher unemployment represent a failure of the economy, not a failure of antipoverty policies. Most economists agree that several factors have contributed to wage stagnation and increasing earnings inequality. These include labor-saving technological changes, the globalization of labor and product markets, immigration of less-educated workers, the declining real value of the minimum wage, and declining unionization. This evidence refutes President Reagan’s view that poverty remains high because the safety net provided too much aid for the poor and thus encouraged dysfunctional behaviors. Studies do show that poverty would be somewhat lower if fewer low-skilled men had withdrawn from the labor market, if marriage rates had not declined so much, and if there had been less immigration of workers with little education. But these effects are small compared to the role of turbulent labor markets, slower growth, and rising inequality. (Mis)measuring Poverty The poverty-fighting role of the safety net can only be revealed by using a more accurate poverty measure. The official poverty rate is so high in part because it does not actually count many of the benefits now provided to the poor, especially noncash benefits and refundable tax credits. One reason that Reagan’s critique of the safety net resonates with the public is that the official poverty measure, the main statistical tool to gauge progress against poverty, understates the effects of government programs. The official measure was adopted in the late-1960s to represent the income necessary to provide a minimally decent standard of living. The poverty line varies with family size. For example, in 2012, it was $11,011 for an elderly person and $23,283 for a married couple with two children. Each year, this official statistic provides the main message to policymakers and the public about trends in poverty, even though many have questioned whether a minimally decent standard of living can mean the same thing today as in the mid-1960s.7 Yet, the measure has not been updated for
almost 50 years. Wherever the poverty line is set, however, the poverty rate should be based on a full accounting of family resources. Families are considered poor under the official measure if their money income from all sources and all family members falls below the line. Money income includes wages and salaries, interest, dividends, rents, cash transfers from the government, such as social security and unemployment insurance, and other forms of pretax cash income. The official measure excludes non-cash benefits such as those from the Supplemental Nutrition Assistance Program (SNAP, formerly food stamps) and refundable tax credits such as the Earned Income Tax Credit (EITC). Noncash benefits were not common when the official poverty line was developed, but they have grown rapidly in recent decades. The Census Bureau has developed a “Supplemental Poverty Measure”–or SPM–in response to the recommendations of a National Academy of Sciences panel on how to better measure poverty.8 The SPM has been released for each year since 2009.9 It does count all the resources we channel toward ameliorating poverty, such as SNAP and the EITC. According to the SPM, poverty has increased slightly from 15.1 in 2009 to 16.0 in 2012. Recently, researchers at Columbia University estimated the SPM for every year from 1967 to 2012. They document the importance of counting all benefits the poor receive.10 They estimate what the poverty rate would have been in the absence of (1) the cash safety net programs that are counted in the official measure (OPM); and (2) all the safety net programs, including near cash benefits and refundable tax credits. In Figure 5, we show the percentage of all persons removed from poverty by safety net programs according to each measure. In the left-hand bar, we show the percentage point difference in poverty between the actual OPM and what it would have been if all cash benefits had been “zeroed out;” in the right-hand bar, the analogous difference for the SPM. In 1967, when most safety net benefits were cash transfers (e.g., social security benefits, unemployment insurance, cash welfare), moving from the OPM to the SPM made little difference, as the safety net reduced poverty by about 5 percentage points using either measure. But during subsequent decades, noncash benefits and refundable tax credits grew more rapidly than cash ben-
national report card • The Stanford Center on Poverty and Inequality
poverty efits, with the result that the OPM increasingly understates the “antipoverty impact” of safety net programs. By 2012, according to the OPM, the safety net reduced poverty by 9 percentage points; but the SPM shows that the full safety net reduced poverty by 14.5 percentage points. Thus, the official measure fails to account for about a third of the antipoverty impact of safety net programs.11 To be consistent with the priorities of the War on Poverty planners, Figure 6 maintains the official poverty lines but counts all resources, including noncash benefits and refundable tax credits. According to Arloc Sherman,12 counting these resources reduces the official poverty rate to 10.9 percent in 2011 from 15.0 percent (the difference between the top and bottom lines). This means that the poverty rate would have fallen by 8 percentage points, not 4 points, since 1965. Beneath the Poverty Line: Extreme Poverty and Deep Poverty We now consider measures of extreme and deep poverty. The OPM and SPM focus on a single point in the income distribution. For instance, if the poverty line for a given family is $23,000, the OPM or SPM simply document whether a family falls above or below that line. Over recent decades, however, there have also been substantial income changes among those below the poverty line. In a recent paper, H. Luke Shaefer and Kathryn Edin examine trends in “extreme poverty,” which they define as living on less than $2 a day, the World Bank metric of global poverty.13
Percentage Point Impact of Transfers Under OPM and SPM, 1967-2012
figure 5.
19
They find that, for households with children, extreme poverty based on money income has rapidly increased from 1.7 percent in 1996 to 4.3 percent in 2011. If non-cash benefits and refundable tax credits are counted as income, extreme poverty rises by much less, from 1.1 to 1.6 percent over these years. Thus, even though extreme poverty has increased, the situation would have been much worse without additional resources provided by safety net programs. A similar result holds for “deep poverty,” defined as income less than 50 percent of the poverty line. According to the Columbia study, deep poverty for children would have risen to over 20 percent in some years without government benefits.14 When all safety benefits are counted, however, deep child poverty is around 5-6 percent in almost all years since 1967. Taken together, these studies suggest that safety net programs raise the living standards of millions of people even though they are not always large enough to raise them out of poverty. Where do We Go from Here? Poverty remains high because, since the early 1970s, unemployment rates have been high and economic growth has been less effective in reducing poverty than it was in the quarter century following World War II. Although the economy has largely failed the poor, safety net programs that were introduced or expanded in response to the War on Poverty take more people out of poverty today than was the case in the early 1970s. This increased antipoverty impact is obscured
Poverty Rate Shows Greater Improvements Since 1960s When Non-Cash Benefits are Counted
figure 6.
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12
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percent
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5
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Impact of Transfers (OPM)
Source: Fox et al. (2013)
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2007
Impact of Transfers (SPM)
2012
1960
1968
1976
Official Poverty Measure (cash income)
1984
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Source: Arloc Sherman, Center on Budget and Policy Priorities (2013).
national report card • The Stanford Center on Poverty and Inequality
2008 2012
Counting non-cash benefits: food stamps, rent subsidies, and refundable tax credits (dotted line is CBPP estimate prior to 1979)
20
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because the official poverty measure does not value the poverty-reducing effects of noncash benefits and refundable tax credits. President Johnsons’ vision and policy priorities of 1964 remain relevant today. If poverty is to be significantly reduced, we must find ways to ensure that the benefits of economic
growth are more widely distributed than they have been in recent decades. The best way to do this is to adopt policies to increase the employment and earnings of the poor. Even with such a renewed focus on raising the market incomes of the poor, we must also continue to strengthen the safety net programs to prevent even more families from falling through the cracks. ■
Notes 1. Miles Corak, David Haproff, H. Luke Shaefer, and Jane Waldfogel provided thoughtful feedback on a previous draft. 2. See Danziger and Gottschalk, 1995, and Bailey and Danziger, 2013.
5. Over time, immigrants comprise a larger share of all Hispanics, causing their poverty rate to rise because recent immigrants are more likely to be poor than the native-born. 6. See U.S. Bureau of the Census, 2013.
3. These analyses begin in 1968 given limitations in the available data for earlier years. The chart on race/ethnicity begins in 1970 as it is difficult to identify Hispanics in prior years.
7. See Citro and Michael, 1995.
4. As the population has become more educated, dropouts are an increasingly smaller group. The long-term trend for all persons without a college degree is also toward greater poverty.
10. See Fox, Garfinkel, Kaushal, Waldfogel, and Wimer, 2013.
8. See Citro and Michael, 1995. 9. See Short, 2012.
11. The SPM addresses the effects of having public health insurance, such as Medicaid and Medicare, by subtracting medical out-ofpocket expenses from income. Sommers and Oellerich (2013) estimate the extent to which Medicaid reduces out-of-pocket medical expenses of the poor and conclude that, without Medicaid, an additional 2.6 million persons would have been poor in 2010 according to the SPM. 12. See Sherman, 2013. 13. See Shaefer and Edin 2013. 14. See Fox, Garfinkel, Kaushal, Waldfogel, and Wimer, 2013.
Additional Resources Bailey, M. J., & Danziger, S. (Eds.). (2013). Legacies of the War on Poverty. New York: Russell Sage Foundation. Citro, C. F., & Michael, R. T. (Eds.). (1995). Measuring Poverty: A New Approach. Washington, D.C.: National Academies Press. Danziger, S., & Gottschalk, P. (1995). America Unequal. Cambridge, MA: Harvard University Press. Fox L., Garfinkel, I., Kaushal, N., Waldfogel, J., & Wimer, C. (2013). Waging War on Poverty: Historical Trends in Poverty Using the Supplemental Poverty Measure. Working Paper, Columbia University School of Social Work. Galbraith, J. K. (1958). The Affluent Society. New York: New American Library. Harrington, M. (1962). The Other America: Poverty in the United States. New York: Macmillan.
Johnson, L. B. (1964). Annual Message to Congress on the State of the Union. Available at: http://www.lbjlib.utexas.edu/johnson/archives.hom/speeches.hom/640108.asp King, Miriam, Steven Ruggles, J. Trent Alexander, Sarah Flood, Katie Genadek, Matthew B. Schroeder, Brandon Trampe, and Rebecca Vick. (2010) Integrated Public Use Microdata Series, Current Population Survey: Version 3.0. [Machine-readable database]. Minneapolis: University of Minnesota. Lampman, R. (1959). The low income population and economic growth. U.S. Congress Joint Economic Committee Study Paper no. 12. Washington, DC: U.S. Government Printing Office (USGPO). Reagan, R. (1988). Address before a Joint Session of Congress on the State of the Union. http://www.presidency.ucsb.edu/ws/index. php?pid=36035
Sherman, A. (2013). Official Poverty Measure Masks Gains Made over Last 50 Years. Washington, DC: Center on Budget and Policy Priorities. Available at http://www.cbpp.org/ cms/index.cfm?fa=view&id=4015 Sommers, B. D., & Oellerich, D. (2013). The Poverty-Reducing Effect of Medicaid. Journal of Health Economics, 32, 816-832. U.S. Bureau of the Census (2013). Income, Poverty and Health Insurance Coverage in the United States: 2012. Series P-60245. Available at http://www.census.gov/ prod/2013pubs/p60-245.pdf U.S. Council of Economic Advisers (1964). The Economic Report of the President, 1964. Washington, D.C.: U.S. Government Printing Office. Available at http://www.presidency. ucsb.edu/economic_reports/1964.pdf
Shaefer, H. L. and Edin, K. (2013). Rising Extreme Poverty in the United States and the Response of Federal Means-Tested Transfer Programs. Social Service Review, 87, 250-268.
national report card • The Stanford Center on Poverty and Inequality
national report card
safety net
safety net
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January 2014 The Stanford Center on Poverty and Inequality
B y K a r e n J u s k o a n d K at e W e i s s h a a r
Key findings • In 2012, U.S. safety net programs provided about onethird of the income support that low-income households needed to reach 150 percent of the official poverty line. • The poverty relief provided by the safety net increased substantially during the Great Recession and reached its all-time high of 36 percent in 2010. • There is considerable interstate variability in the amount of poverty relief provided by the safety net, with lowsupport states (e.g., Texas, Alabama) meeting only about 26 percent of the need and high-support states (e.g., Washington, Massachusetts) meeting as much as 40 percent of the need (based on pooled data from the 2008-2012 period). • The extent to which households lose safety net benefits as their market earnings increase declined dramatically in the early 1990s and has continued to decline gradually thereafter. This change in the rate of “relief falloff” presumably works to incentivize self sufficiency.
he overall effectiveness of the social safety net is difficult to evaluate in the U.S. because our welfare institutions comprise such a complicated amalgam of social assistance and insurance programs. Due to this patchwork approach to meeting needs, low-income families are often obliged to rely on support from many sources, and the task of judging the overall effectiveness of the safety net thus requires assessing the combined effect of all programs. The task of assessing safety net performance is further complicated because the amount of support low-income families secure is often conditioned by a variety of factors in addition to earnings (e.g., household composition).
T
that provides for basic needs—and assess the extent to which American safety net programs are successful in raising the incomes of the poor up to this threshold. We should be concerned if, for example, income support is so minimal, or so inefficiently targeted, that it makes up only a small part of the difference between the earnings of a poor household and its poverty threshold. This would imply that, even with safety net support, lowincome households are unable to meet basic needs. Alternatively, if safety net programs typically raise the total incomes of poor families to a level at which basic needs can be met, then we might characterize them as relatively successful in providing relief.
For these reasons, a focus on one program or a single source of support provides an incomplete, and potentially misleading, evaluation of the safety net. In the U.S., each safety net program has a different constellation of beneficiaries and a distinctive funding trajectory, thereby making the overall trend in safety net effectiveness a complicated function of a mixture of program effects. It is all too easy to be misled by the funding vagaries of any particular program and thereby miss the big picture of whether the safety net, as a whole, is working as we would like it to work. In this brief, therefore, we use a total-income-based measure of the effectiveness of the American safety net, a “poverty relief ratio” (R), to provide an overall assessment of the effectiveness of our social safety net.
The first and key objective of this brief is to assess, therefore, whether the safety net is efficiently delivering on the simple objective of reducing poverty. But we also care about how this objective is—or is not—being met. Historically, the safety net has been evaluated not just in terms of its effectiveness in directly eliminating poverty in the short run (via transfers), but also in terms of its success in incentivizing families to secure income in the labor market and reducing, over the long run, the very need for transfers. We of course want a safety net that provides the necessary temporary support while also encouraging families to become self sufficient.
We apply here the standard concept of a poverty threshold—an amount of income
In this brief, we therefore adopt a conventional two-pronged assessment of the safety net, with the following questions serving as the focus of our analyses:
22
safety net
• How has the country fared over time in its commitment to provide basic income support to those who are very poor (e.g., the "baseline relief" parameter)? • To what extent does policy incentivize efforts to increase market income by minimizing the rate of falloff in transfers as income grows (e.g., the "relief falloff" parameter)? We address these questions with data collected from the March Supplement of the Current Population Survey. These data can be used to track national trends as well as interstate differences in poverty relief. We will monitor changes in poverty relief for the U.S. as a whole between 1988 and 2012, and we will also compare levels of poverty relief across the U.S. states (using pooled data pertaining to the years from 2008 to 2012). What do we find? Most importantly, the effectiveness of American safety net programs remains somewhat limited, although there have been significant improvements in the provision of income support for low-income households over the last 25 years. We find especially large increases in the overall effectiveness of American safety net programs following the passage of the American Recovery and Reinvestment Act of 2009. Nevertheless, using a standard poverty threshold (i.e., 150% of the 2010 official poverty line), in 2012 American safety net programs provide only an average of about 32 percent of the income support low-income households would need to have a total income equal to this poverty threshold. We also find sizable cross-state variation in the effectiveness of the safety net. For the 2008-2012 period, some states provide only a quarter of the income support needed to raise the income of low-income households to the poverty threshold, while others provide 40 percent of the needed relief. The poverty relief ratio tends to be highest in the West and Northeast, middling in the Midwest, and lowest in the South and some of the interior states. The second parameter of interest is the rate by which antipoverty relief falls off as households secure more market income. Here again we find evidence of substantial change between 1988 and 2012. The rate of falloff was dramatically reduced in the early 1990s and then declined far more gradually thereafter. Although the “relief falloff” parameter is thus declining within the U.S. as a whole, there remains substantial cross-state variability in this parameter. For example, Arizona has a sharp falloff in relief, while Connecticut has a far flatter
rate of falloff that—presumably—better incentivizes efforts to increase self sufficiency. The evidence behind these and other key conclusions is laid out below. The first section outlines the challenges associated with evaluating safety net programs in the U.S. and makes a case for a total-income measure. We next present estimates of the poverty relief ratio, and its component parts, for the U.S. during the 1988-2012 period. Then, we turn briefly to the states, identifying those that are more (and less) successful in poverty relief. Finally, we anticipate how recent changes in support for the long-term unemployed will affect our estimates of poverty relief in the near future. Measuring Poverty Relief Figure 1 reports average levels of income support provided to low-income households using the Current Population Survey (see “Data Processing Notes” for details on data and methods). All amounts are reported in thousands of 2012 U.S. dollars for equivalent-sized households (i.e., total dollar amounts are divided by the square root of the number of people in each household). Income support is divided by type, into social insurance (unemployment, disability, and worker’s compensation), social assistance (welfare, Supplemental Nutrition Assistance Program or food stamps, Supplemental Security Income, and other programs with minimum income provisions), and “Earned Income Tax Credit” (EITC), a refundable tax credit predominantly for low-income families (with eligibility determined by income, marital status, and the number of children). Notice, first, that there are big differences in the amount of support that low-income households receive: Households with no market income receive, on average, approximately 50 percent more than is received by those in the adjacent income categories (representing very little market income). We may conclude that the safety net is oriented toward assisting zeroincome households. Second, the sources of support vary across income groups, too. Not surprisingly, social assistance programs provide support mainly to those households with very low market earnings. By contrast, EITC goes mainly to those earning slightly more, but still low incomes. Households earning between five and ten thousand dollars receive, on average, about one thousand dollars through EITC, while households earning fifteen thousand dollars receive, on average, only a few hundred dollars.
national report card • The Stanford Center on Poverty and Inequality
safety net It is of course well known that low-income households benefit from a variety of safety net programs, and to varying extents. However, the measures policy analysts use to evaluate safety net programs do not adequately take these simple facts into account, as they are typically oriented to questions other than the effectiveness with which the safety net reduces poverty. There are, for example, three classes of frequently-used measures that are not adequate for our purposes: • Fiscal measures represent the gross size of government allocations to programs, but provide little information on who receives how much support and whether it significantly changes their circumstances. • Redistributive measures, like changes in income inequality after tax and transfers are applied, reflect the effects of redistribution on the overall income distribution rather than changes in the conditions of the poor in particular.
figure 1.
• Behavioral measures reflect changes among program recipients in, for example, rates of labor market participation or receipt of social assistance and thus again do not speak directly to the economic circumstances of recipients. By contrast, poverty rate reduction measures estimate changes in the proportion of households that live in poverty, making them most similar to the measure we present here. However, conventional poverty rate reduction measures are not adequate for our purposes, as simple changes in poverty rates can conceal important changes in the distribution of support among low-income households. For example, a policy change may increase support for those with little or no market income, without changing the share of households living below some poverty threshold. More importantly, poverty reduction rate measures vary with the poverty threshold. The measure we present here, instead, maintains the relative ordering of states or annual observations across reasonable
Social Transfers, by Type and Market Income (2010).
5
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social transfers (in $ thousands)
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NOTE. This figure reports average social transfers, by equivalent-household income level, and by type: social assistance programs, social insurance, and EITC. Each bar represents one percent of the national distribution. Source: CPS 2010.
national report card • The Stanford Center on Poverty and Inequality
50
24
safety net
poverty thresholds, and is accordingly especially well-suited to comparative analysis.
those at higher levels of market income, this variation is less relevant to this discussion.
We therefore use the relationship between market income and social transfers as the basis of a poverty relief ratio. Notice in Figure 2, which plots (equivalent household) social transfer amounts against market income for three states, that this relationship varies on two important dimensions: First, California, Florida, and Texas differ in levels of income support that they provide to households with zero market income. We refer to this as “baseline relief.” Second, states also vary in the rate at which benefits decline with small increases in market earnings. Presumably, where this “relief falloff” is the greatest, incentives to increase market earnings are significantly undermined: For the very poor, small increases in earnings may result in dramatic decreases in income support, and consequently in total income. Where relief falloff is less dramatic, very low-income families continue to receive income support as they increase their market earnings, and therefore will likely have stronger incentives to enter the labor force. While states also vary in the amount of income support they provide (largely through unemployment insurance) to
We pay particular attention to these first two differences in the relationship between market income and social transfers: differences in baseline relief, and differences in relief falloff. In fact, the parameters that describe the general relationship between social transfers and market income (the solid blue lines in Figure 2) can be used to estimate baseline relief and relief falloff directly, and can be used as the basis for a comparison of poverty relief within a state over time, or across societies more generally. (see ``Deriving the Poverty Relief Ratio (R)’’ for more details).
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The General Relationship between Social Transfers and Market Income (2008-2012).
16
figure 2.
While the variation in levels of baseline relief and relief falloff are interesting and informative themselves, a cross-state or time series comparison based on only one (or even two) of the parameters would be an incomplete analysis of poverty relief. Measures based on the benefits received by any particular low-income household would be similarly misleading. Instead, we use the relationship between social transfers and market income to generate an estimate of the amount of
0
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NOTE. This figure reports average social transfers, by equivalent-household income level. Each data point represents one percent of the pooled 2008-2012 state sample. All amounts reported in 1999 USD. Source: CPS 2008-2012.
national report card • The Stanford Center on Poverty and Inequality
safety net income support provided relative to what is needed to bring all low-income households up to a poverty threshold. We use 150% of the 2010 official poverty line, for equivalent households, or $16,584, as our benchmark. The red lines in Figure 2 represent the amount of income support that would be necessary to raise the income of each low-income household to $16,584. Then, we estimate the ratio of the area under the solid blue line in Figure 2 (the estimated relationship between social transfers and market income) to the area of the triangle completed by the solid brown line. An estimate of R=.32 (the state mean for the 2008-2012 period), for example, implies that an average low-income household could expect to receive about 32% of the income support it would need for its total income (market income plus social transfers) to equal the poverty threshold. The National Estimates We are now in a position to examine trends in poverty relief. Figure 3 reports estimates of the Poverty Relief Ratio, R, and its components, baseline relief (middle panel) and relief falloff (bottom panel), for the U.S. from 1988 to 2012. Increases in R (top panel) correspond to increases in income support, relative to the poverty threshold. Similarly, increases in baseline relief correspond to increases in support for those households with no market income. Finally, when the relief falloff parameter becomes less negative, it means that a given increase in earnings leads to a less substantial decline in benefits (with the presumption that the disincentive to pursuing market earnings is thereby reduced). Focusing first on the top panel, we observe major shifts in overall levels of poverty relief during this period: During the late 1980s and early 1990s, average levels of income support provided slightly more than a fifth of what was needed to raise the total income of poor families to the poverty threshold. By 2012, income support had increased to 32 percent of what is needed to raise poor families’ incomes up to the poverty threshold. Major changes in R correspond to important policy shifts. We observe an increase in the effectiveness of safety net programs with the expansion of the EITC in 1990 and especially in 1993, when President Clinton made the EITC the cornerstone of his antipoverty program. Then, following the implementation of the Personal Responsibility and Work Opportunity Act in 1997, we see a significant decline in levels of income support provided to low-income households. We also observe a slight increase in benefits in the early years of the Bush
Deriving the Poverty Relief Ratio (R)
› The solid blue lines in each panel of Figure 2 report the estimated relationship between social transfers and market income. This relationship is generally well-described by a negative exponential function, STij = αj + β1j exp(β2jMIij) + eij
(1)
where STij denotes social transfer amounts, MIij denotes market income for individuals i = 1…n in states j = 1…J, the parameters αj > 0, β1j > 0, and β2j < 0 describe the bivariate relationship within each state, and eij is a stochastic residual term.
› Notice that individuals who have no market income (i.e., MIij =0) receive, on average, income support in the amount of αj + β1j (“baseline relief”). Similarly, for very high levels of market income, STij is expected to take on the value αj. Finally, β2j reports the curvature of the line, or the rate at which benefit levels decline with increased market earnings; we refer to this as “relief falloff.” › The solid brown line in Figure 2 reports the linear function, STij =ψ−MIij.
(2)
› Here, ψ is a poverty threshold (e.g., a household equivalent of 150% of the official poverty line), and ST and MI are social transfers and market income, respectively. The expression in Eq. (2) reports the amount of income support that would need to be provided to raise the total amount of income, for all low-income households, to the poverty threshold, ψ.
› In combination with Eq. (1), we can calculate the poverty relief ratio R as an estimate of the amount of income support needed, relative to the total amount implied by Eq. (2), that would bring the total income of each low-income household to the poverty threshold, ψ.
national report card • The Stanford Center on Poverty and Inequality
25
26
safety net to an increase in values in the bottom panel) from the late 1990s through 2012. Most of the change that we observe in overall levels of poverty relief after 1997 can be attributed to changes in baseline relief.
administration as part of the post-9/11 economic stimulus. Finally, following the 2008 financial crisis, we observe some success in the Obama administrationâ&#x20AC;&#x2122;s efforts to provide poverty relief, as levels of poverty relief increase to 36 percent of the poverty threshold.
In light of Figure 3, how then might we assess the effectiveness of our safety net? The first point that can be made is that the safety net did much work reducing the impact of the Great Recession on the amount of poverty. We see a substantial uptick in R during the recession years and, in this sense, the U.S. safety net responded just as it should have responded. At the same time, it is hardly the case that the safety net is eliminating all poverty (at least as measured here), indeed there remains much unmet need even after the safety net has Estimates of R, Levels of Baseline Relief, and Relief Falloff (1988-2012) acted.
The shifts in R that we observe in the top panel can be attributed to changes in baseline relief as well as changes in relief falloff. As EITC expands during the early 1990s, we see a dramatic decline in the rate at which safety net support drops off with increases in earnings. Then, we observe a steady but important decrease in the rate of relief falloff (corresponding
.4
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.5
figure 3.
.36 .35
.3
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.19
.29 .28 .29 .29 .28 .27 .27 .28 .27 .27 .27 .28
.32
.21 .22
.1
.2
.21
.31 .31 .31 .31 .31
The second point is that we have fashioned a safety net in which the rate of relief falloff is gradually declining. Taken as a whole, our safety net is therefore increasingly operating to incentivize market work, which is precisely the type of safety net that most people want.
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2012
NOTE. This figure reports estimates of R (top panel), baseline relief (middle panel), and relief falloff (bottom panel) for the U.S., for 1988-2012.
State-Specific Estimates The foregoing national estimates conceal much state-level variability in the amount of relief and how it is provided. To cast light on this variability, Figure 4 maps the distribution of the poverty relief ratio across the U.S. states. We observe some regional clusters, with states in the West and Northeast generally providing more effective income support, and states in the South and interior providing more limited poverty relief. We know from the analysis presented in Figure 3 that most of the variation in the effectiveness of statesâ&#x20AC;&#x2122; poverty relief programs comes from variation in baseline support. To cast further light on this variability, Figure 5 next plots the estimates of baseline relief against relief falloff. Higher values on the vertical axis correspond to higher levels of baseline relief. Increasing values on the horizontal axis correspond to lower rates of relief falloff. Those states, like Wyoming, Florida, and Nebraska, in the
Source: CPS 1988-2012.
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safety net lower left quadrant of the graph provide low levels of support that drop off quickly with small increases in income. States in the upper right quadrant, like Massachusetts, Maine, and Rhode Island, provide relatively high levels of support to those with no market income, while benefits in these states decline comparatively slowly with small changes in market income. The third group of states, those in the upper left quadrant, like Washington and California, are those states that provide relatively high levels of support to no-market-income households, but benefits drop off fairly quickly with earnings. Finally, those states in the lower right quadrant, like Kentucky, provide relatively little income support to those with no market income, and instead provide a more uniform distribution of benefits (i.e., most income support is provided through unemployment insurance programs). If the results of Figures 4 and 5 are combined, one finds that there are two roads to securing high poverty relief. The road typically taken in the Western states (e.g., Washington, California, Nevada, Utah) is to combine high levels of baseline relief with a relatively steep falloff, while the road typically taken in the Eastern states (e.g., Massachusetts, Maine,
figure 4.
27
Rhode Island) is to combine high levels of baseline relief with a less pronounced falloff. Although those who prefer low-disincentive regimes would presumably opt for the Eastern road, it bears noting that, at least by the standard of overall poverty relief, each approach is doing substantial work. Conclusions Building on our earlier work, we have used the poverty relief ratio to provide a direct measure of the effectiveness of American safety net programs. Implicitly, the poverty relief ratio identifies a goal for American social policy â&#x20AC;&#x201C; raising all income levels to a well-specified poverty threshold â&#x20AC;&#x201C; and tracks progress towards this goal. As this analysis makes clear, there is much work to be done: In 2012, only 32 percent of the total need was met (using a benchmark of 150% of the 2010 official poverty line). In some of the Southern states, the poverty relief ratio was especially low, dropping down to as little as 26 percent. At the same time, the safety net responded rather effectively to the challenges of the Great Recession, indeed the poverty relief ratio reached an all-time high of 36 percent in 2010.
Estimates of R, by State (2008-2012)
Poverty Relief Ratio (R) 0.26-0.29 0.29-0.31 0.31-0.33 0.33-0.40
NOTE. This Figure reports estimates of R for pooled 2008-2012 samples for each state. Source: CPS 2008-2012.
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safety net a gradual increase over the last two decades in the relief falloff parameter.
Why has the poverty relief ratio increased during the recessionary and post-recessionary period? The answer is twofold: The recessionary labor market generated precisely that type of need (e.g., unemployment) that our safety net was relatively well-equipped to handle, and the safety net has been further modified and extended to cover additional types of need (e.g., more protracted periods of unemployment) that had not before been covered.
We can anticipate, finally, how very recent shifts in policy are likely to affect our estimates of poverty relief in the near future: Dramatic cuts in long-term unemployment benefits, and in Supplemental Nutrition Assistance Program (SNAP), are likely to be apparent in decreases in baseline relief, and possibly in relief falloff, which will both work to lower the overall amount of poverty relief. If levels of poverty relief return to pre-2009 levels, as seems likely, the consequences of the federal sequester are likely to be problematic for low-income families. â&#x2013;
It also bears noting that the safety net is increasingly taking a shape that incentivizes labor market attachment. This transition was most dramatic, of course, with the expansion of EITC in the early 1990s. But it continues apace in the form of
Characterizing State Income Support Programs, 2008-2012
6
figure 5.
5
WA NJ PA
Baseline Relief 3 4
NV
CA NY
CO
DC
UT
IA SC OK
FL
NE
IL NC
ME WI CT OR OH IN
HI
ID DE MNMT WV AK TN SD GA MD MO NH LA MS KS AR NM VA TX AL KY ND
2
WY
RI
MI
VT
AZ
MA
-.13
-.11
-.09 Relief Falloff
-.07
NOTE. This figure reports estimates of the parameters in Eq. (1), for pooled 2008-2012 samples for each state. Solid lines correspond to median values in each dimension. Source: CPS 2008-2012.
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Notes 1. Please note that we use the term “income support” although our measure of social transfers also includes near-cash benefits (Supplemental Nutrition Assistance Program, or food stamps, and energy assistance). 2. See Jusko and Weisshaar, 2013, for a full and more technical treatment.
Additional Resources Buhmann, B., Rainwater, L., Schmaus, G. and T. Smeeding. 1988. “Equivalence scales, well-being, inequality, and poverty: sensitivity estimates across ten countries using the Luxembourg Income Study (LIS) database.” Review of Income and Wealth 34:115-42. Jusko, K. L.. 2008. “The Political Representation of the Poor.” A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Political Science) in The University of Michigan.
Jusko, K. L. and K. Weisshaar. 2013. “Measuring Poverty Relief.” Working paper. Stanford University. O’Hara, A.. 2006. “Tax Variable Imputation in the Current Population Survey” in the IRS Research Bulletin (Publication 1500), 169-82. US Census Bureau. 2010. “Poverty Thresholds for 2010 by Size of Family and Number of Related Children Under 18 Years.” Table published on-line at http://www.census. gov/hhes/www/poverty/data/threshld/.
Data Processing Notes This analysis is based on the Current Population Survey March Supplement, household income data.
The analysis is restricted to working-aged households (i.e., in which the head of household is aged at least 25).
All dollar amounts are reported in thousands of 1999 USD. Except for EITC, which is estimated by the US Census Bureau on the basis of the information provided (see O’Hara 2006 for more detail), all income amounts are reported by CPS survey respondents. To generate equivalent household estimates of earnings and transfers, total dollar amounts were divided by the square root of the number of people in each household (see Buhmann et. al. 1988).
Market income includes wage and salary, self-employment, farm, interest, dividend, rent, child support, alimony, veteran’s, pension/ retirement, and familial assistance income. Social assistance support includes welfare (Temporary Assistance to Needy Families, TANF, and its predecessor Aid to Families with Dependent Children, AFDC), Supplemental Security Income, Supplemental Nutrition Assistance Program benefits (SNAP), energy assistance, and other means-tested income support programs.
Tax credits include “Earned Income Tax Credit” (EITC), a refundable tax credit predominantly for low-income families (eligibility is determined by income, marital status, and the number of children). Social insurance benefits include unemployment insurance, as well as disability insurance and workers’ compensation. A common poverty threshold, ψ= $16,584 (150% of the 2010 poverty threshold for a family of four, divided by 2; see US Census Bureau 2010) is used in calculations of R.
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income inequality
income inequality January 2014 The Stanford Center on Poverty and Inequality
By Jeffrey Thompson and Timothy Smeeding
month & year
Recessionary Periods
Median Household Income (left)
Source: John Coder and Gordon Greene, Sentier Research, Annapolis Maryland, 2013
national report card • The Stanford Center on Poverty and Inequality
Unemployment Rate (right)
oct 13
oct 12
apr 13
2 oct 11
$50 apr 12
3
oct 10
$51 apr 11
4
oct 09
$52
apr 10
5
oct 08
$53
apr 09
6
oct 07
$54
apr 08
7
oct 06
$55
apr 07
8
oct 05
$56
apr 06
9
oct 04
$57
apr 05
10
oct 03
$58
apr 04
11
oct 02
$59
apr 03
12
oct 01
$60
seasonally adjusted unemployment rate
Median Household Income and Unemployment Rate: Jan. 2000 to Oct. 2013
apr 02
figure 1.
oct 00
• The tax, transfer, and other economic policies adopted to fight the Great Recession did blunt the impact of job losses on income and consumption. Not all populations were shielded by these measures equally, however: For example, most measures of post-tax and transfer income inequality fell among the overall population during the Great Recession, whereas the same measures were either flat or slightly rising for non-elderly households.
apr 01
• In the recovery period since mid-2009, all of these measures now show inequality is rising. By 2012, the share of income of the top one percent had rebounded, nearly returning to the high levels from before the Great Recession.
As will be shown, the record is perhaps more complicated than is often appreciated, with different measures of inequality yielding different conclusions about the effects of the Great Recession. We will lay out the discrepancies and conclude by suggesting that they arose, in part, from the highly targeted effects of the policy response to the Great Recession, and, also in part, because the capital income sources so important to affluent households declined sharply in the Great Recession.
The economic crisis of 2008-09 resulted in millions of lost jobs and billions in lost wealth, caused poverty to rise dramatically, and led to a fall in household incomes. Now four and a half years after the end of the Great Recession, the ensuing recovery has left unemployment high for an extended period and has been slow to restore income growth for most households, especially
apr 00
• The Great Recession had a mixed effect on inequality: Although it brought about an increase in standard household income-based measures (e.g., the Gini coefficient), it led to a flattening of consumption inequality as well as a decline in the income share going to top-income households.
T
Median Household Income in Thousands (Oct 2013$, seasonally adjusted)
Key findings
those in the middle of the distribution. This brief not only reexamines this recent record but also considers the impact of recent policies on inequality and speculates on where inequality may be heading from here.
he takeoff in income inequality over the last four decades is by now well-known. By contrast, the public is perhaps less familiar with changes in the income distribution over the last decade, a period marked by a financial crisis, the Great Recession, and the tepid recovery. The main purpose of our brief is to review this more recent record.
income inequality
Median Household Income It is useful to begin by considering recent changes in the central tendency of the income distribution. As shown in Figure 1, median household income started to deteriorate after the labor market fell into recession in 2008. Shortly before the official onset of the Great Recession, in July of 2007, the seasonally adjusted unemployment rate was 4.7 percent, and median household income was $56,100. Two years later, the recession was determined to be over, as GDP growth and other economic indicators had recovered, but unemployment remained high, at 9.5 percent, and median household income was $54,250. How has median income fared in the recovery period? As unemployment remained stubbornly high (above 9 percent) for most of 2010 and 2011, median household income continued to fall. It hit a low-point in mid-2011, roughly ten percent lower than pre-recession levels. After mid-2011, the unemployment rate drifted down toward eight and then seven percent, and median household income began to slowly grow. By October 2013, however, nearly five years after the end of the Great Recession, median income remains seven percent below pre-recession levels, at $52,300.
figure 2.
The Distribution of Income Median income declined in the Great Recession and has only slowly recovered since, but the experience of the typical household was not shared by all households. Figure 2 presents income shares of all five quintiles from 1967 to 2012 using household-size adjusted data from the Census Bureau. The figure is anchored at 100 percent in 1967 to highlight changes in income shares over time. Figure 2 tells a tale of divergence during the period from 2007 to 2010. The share of income received by the bottom three quintiles of the distribution declined between 2007 and 2010 by at least 10 percent; the fourth quintile barely held its own; and the share of the top quintile continued to rise. The largest declines during this period were experienced at the bottom of the distribution: the share of the lowest-income quintile fell from 3.8 percent to 3.4 percent, and the share of the second quintile fell from 9.5 percent to 9.2 percent. In 2009-10 the bottom three quintiles reached all-time lows. The fourth quintile showed little change, but the top quintile share rose from 48.5 to 49.2 percent of total income in 2010. The shifting income shares in the Great Recession, mainly due to job losses that most dramatically damaged income at the bottom of the distribution, accelerated long-term trends that have been unfolding since the 1980s.
Percent Change in Shares of Adjusted Household Income by Quintile (Share of Income of Each Quintile Relative to Share in 1967)
30 20 10
percent change
0 -10 -20 -30 -40 -50 1968
1972
Lowest Quintile
1976
1980
Second Quintile
1984
1988
Middle Quintile
31
1992
1996
2000
Fourth Quintile
Source: DeNavas-Walt, Proctor, and Smith (2013), Table A-2, pages 40-44.
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2004
Top Quintile
2008
2012
income inequality
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Do the same trends continue on during the three post-recession years? Indeed this general pattern of rising inequality continues even during the recovery. The middle three quintiles saw small declines in their share of income between 2010 and 2012, while the share of the top quintile rose from 49.2 percent to 49.9 percent.1
Figure 3 presents additional time series of Gini coefficients based on data from the Consumer Expenditure Survey (CEX). This survey is useful because it allows us to compare income and consumption measures of inequality. As Figure 3 shows, the Gini coefficient for CEX income rises from .423 to .427 between 2007 and 2009, and climbs to .435 by 2012.3
The data presented in Figure 2 are instructive, but it is well to bear in mind their limitations. Most importantly, the Current Population Survey (CPS) definition of “Money Income” includes cash transfers but does not exclude taxes, which means that it understates available resources for poorer families by virtue of ignoring near-cash transfers and refundable tax credits, yet overstates them for some families by virtue of ignoring taxes. This series also does not allow us to identify changes occurring at the very top of the distribution. In the following sections, we present other data series that address some of these limitations.
The story to this point has thus been a straightforward one of mainly rising inequality during the recession and recovery. If we instead focus on disposable income, which includes transfer income and subtracts federal and state taxes paid, we find that inequality did not rise in the Great Recession.4 The Gini coefficient for disposable income fell slightly from .372 to .370 between 2007 and 2009. It then rose after 2009, but the increase was only half as large as the increase in pre-tax income. The same caveat holds for consumption inequality: The Gini coefficient for consumption, again drawn from the CEX, fell from .291 to .283 between 2007 and 2009 (see Figure 3).
Gini Coefficients for Income and Consumption The Gini coefficient, the most commonly used income distribution statistic, is of course important to consider as well. It is reassuring that this measure also indicates that the inequality of pre-tax income rose in the Great Recession. Using CPS “Money Income,” the Gini rose from .463 to .468 between 2007 and 2009, and then continued to rise in 2012, reaching .477.2
figure 3.
The various series on inequality thus diverge somewhat in the story they tell about the time period near the Great Recession. They do not diverge to the same extent for other time periods. As Fisher, Johnson, and Smeeding note,5 the consumption and income inequality measures track very closely between 1985 and 2006, at which point they diverge. It is only
Gini Coefficients for Income and Consumption (Fisher, Johnson, and Smeeding, 2013a)
.45 .43 .41 .39
COEFFICIENT
.37 .35 .33 .31 .29 .27 .25 1984
1986
Income
1988
1990
Disposable Income
1992
1994
1996
1998
2000
2002
2004
Consumption
Source: Fisher, Johnson, and Smeeding (2013) based on analysis of CEX data, updated
national report card • The Stanford Center on Poverty and Inequality
2006
2008
2010
2012
income inequality immediately prior to and during the Great Recession that the income and consumption measures give a different impression of the trajectory of inequality, owing in great measure to a decline in spending at the top of the distribution. In the recovery after the Great Recession, the cross-series consensus has returned, with both income and consumption measures showing rising inequality. Top Shares of Income We now turn to measures of inequality using the share of income captured by the very top of the distribution. The key conclusion from these measures: The trend during and after the Great Recession is similar to that which we have just seen for consumption inequality. Figure 4 shows trends in the share of income received by the top one percent of income using three different data sources with different income definitions. The top line in Figure 4, drawn from the research of Emmanuel Saez,6 relies on Internal Revenue Service (IRS) tax statistics. It shows that, after rising over most of the preceding three decades, the taxable income share (including capital gains) of the richest one-percent declined from 23.5 percent in 2007 to 18.1 percent in 2009. This drop reflects the massive drop in stock values, earnings, and profits; falling business and asset income,
Top 1 Percent Share (1979-2012) Using Different Income Concepts (Thompson and Smeeding, 2013)
figure 4.
including capital gains, accounts for 80 percent of the decline in income for the top one percent between 2007 and 2009.7 As the economy and the stock market recovered, top income shares have rebounded, rising to 22.5 percent in 2012. Taxable incomes of the top one percent grew 31 percent from 2009 to 2012, while the income of the rest of the distribution grew only by 0.4 percent. It follows that the top one percent captured 95 percent of all income growth in the first three years of the recovery, as profits and equities rebounded strongly, but not wages.8 Using data from the triennial Survey of Consumer Finances (SCF), with a sampling strategy designed specifically to reach high net-worth households, Thompson and Smeeding9 also find that the income share of the top one-percent rose in the 1990s and fell sharply in the Great Recession. The top one percent share of SCF income fell from 21.3 percent in 2006 to 17.2 percent in 2009 (see the brown dots in Figure 4). Like the tax data analyzed by Emmanuel Saez, SCF income also includes capital gains income. Neither income measure, however, includes any of the unrealized gains resulting from the ownership of assets. These gains might be relevant for the distribution of income, as most gains are not realized every year, and the ownership of assets is even more unequally
figure 5. P90/P10 Ratio for All Ages and Non-elderly (indexed 2000=100) Using Disposable Household Income (“equivalized” for household size) (Thompson and Smeeding, 2013)
25
118
20
114 112
15 percent
share of total income received by top 1%
116
110 108
10 106 104
5
102 0
100 1980
1984
1988
1992
Saez (IRS data with KG) SCF income
1996
2000
2004
CBO (pre-tax)
2008
2012
CBO (after-tax)
SCF “MCI” (updated)
Source: Emmanuel Saez (2013), Congressional Budget Office (2013), Smeeding and Thompson (2011) updated.
33
2000
2003
2006
Non-Elderly
2007
2008
2009
All Ages
Source: Thompson and Smeeding (2013), authors’ analysis of CPS.
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2010
2011
34
income inequality
distributed than income. To take such gains into account, Smeeding and Thompson develop a method that estimates unrealized income flows to the assets recorded in the SCF.10 This “More Complete Income” (MCI) concept indicates that the top-income shares are larger once flows to wealth are accounted for, but the trend is largely similar to SCF income and taxable income from the IRS. The top one percent share of MCI declined from 22.4 percent in 2006 to 19.4 percent in 2009 (see Figure 4). The remaining series in Figure 4 pertain to the “comprehensive income” measure developed by the Congressional Budget Office (CBO). This measure does not include unrealized capital income, but it does include estimated values for employer-provided health insurance benefits as well as the in-kind health insurance benefits received through the Medicare and Medicaid programs.11 The top one percent share of the CBO (pre-tax) income fell from 18.7 percent in 2007 to 13.3 percent in 2009, before rebounding to 14.9 in 2010. Overall, the CBO measure has followed the same longer-term (and cyclical) trends as the IRS and SCF-based measures, but has not risen as much over time because the increasing costs of health care are incorporated into their “comprehensive income” measure. Impact of Policies on Inequality An important effect of tax and transfer policies is that they equalize the distribution of income. The effect of taxes can be seen directly in Figure 4, where the top one percent share of CBO comprehensive income is between one and two percentage points lower each year once federal taxes paid are subtracted.12 The bottom of the income distribution benefits from tax and transfer policy. In an analysis of the March CPS, Larrimore, Burkhauser, and Armour find that taxes and transfers offset more than half of the market losses experienced by the lowest-income quintile in the Great Recession.13 These tax and transfer policies also influenced inequality trends in recent years. As seen above in Figure 3, the Gini coefficient for income (using the Consumer Expenditure Survey data) rose nearly three percent between 2007 and 2012, while the Gini for disposable income rose less than one percent.14 Tax and transfer policies were more effective in restraining the growth in inequality for some groups than for others. As Thompson and Smeeding show, the transfer income of the
elderly plays an important part in the decline in the overall disposable income measures; among non-elderly households inequality of disposable income did not fall.15 Figure 5 reveals that the income ratio between the ninth and first deciles (i.e., the P90/P10 ratio) was unchanged during the Great Recession for non-elderly households, but declined nearly three percent once elderly households were included.16 In the recovery period, between 2009 and 2011, the divergence is even more dramatic, as the P90/P10 ratio rose four percent for all households, but nine percent among the non-elderly. Looking Forward This brief has shown that the effects of the Great Recession on income inequality differ across different measures of inequality. Although the Great Recession brought about an increase in inequality for standard household income measures, it led to a flattening in consumption inequality as well as a decline in the income share going to top-income households during the Great Recession period. The decline in consumption inequality is partly attributable to declining consumption at the top of the distribution, as high-income households worked to rebuild assets that were lost in the financial crisis, and to tax and transfer policy that especially benefited the poor.17 If there is cross-series disagreement about the effects of the Great Recession, there is no disagreement about what is happening in the recovery period. Since mid-2009, all measures show that inequality is rising. For example, the share of income of the top one percent had rebounded by 2012, indeed it nearly returned to the high levels from before the Great Recession. The latest, but still early, evidence on the recovery from the Great Recession also points to a very slow rebound of median incomes (see Figure1). Why, it might be asked, is there a divergence in the time series during the Great Recession? Part of the answer is that, for measures that encompass the effects of tax and transfer policy, the especially strong equalizing effect of those policies during the Great Recession worked to offset the ongoing and underlying press toward growing inequality. Also, the business and asset income so important to highincome households declined sharply in 2008 and 2009. As the ambitious set of tax and transfer policies was relaxed in the recovery, and business and asset incomes recovered with capital markets, the longer-term trend toward higher levels of inequality has returned. ■
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income inequality
35
Notes 1. See DeNavas-Walt, Proctor, and Smith, 2013, Table A-2.
7. Authors’ calculations based on Saez, 2013. 8. For details, see Saez, 2013, Figure 2.
2. See DeNavas-Walt, Proctor, and Smith, 2013, page 40. 3. This figure updates Fisher, Johnson, and Smeeding, 2013. 4. Disposable income removes federal and state income taxes and FICA taxes, and adds the value of food stamps and refundable federal tax credits in addition to the other transfer income collected in the survey. State and local sales taxes, however, are not removed. Consumption is spending on all goods and services for current consumption measured in the Consumer Expenditure Survey, excluding life insurance, pension, and cash contributions. For auto and housing purchases, the service flow or rental equivalence are used. See Fisher, Johnson, and Smeeding, 2013, for details. 5. See Fisher, Johnson, and Smeeding, 2013a; 2013b.
9. See Thompson and Smeeding, 2013. 10. We first subtracted reported property income from the SCF, then systematically added back the returns on financial wealth, retirement assets, housing, other investments (including real estate), and business income for owners and proprietors. See Smeeding and Thompson, 2011, for more details. 11. The CBO income measure includes all types of cash and noncash income, employee benefits, realized capital gains, and the burden of all taxes, including tax rebates. See CBO, 2013, for more details. 12. The CBO removes all federal taxes, including the share of corporate taxes attributed to owners of capital, but does not remove any state or local taxes.
6. See Saez, 2013.
13. For details, see Larrimore, Burkhauser, and Armour, 2013. It should be noted that they do not include state and local sales taxes in their measures. This absence is common to all of the other after-tax measures included in this brief, but it does have implications for the after-tax distribution of income, as the effective sales tax burden is greater on lower-income households. Furthermore, in the Great Recession, state governments were most likely to turn to sales and excise tax increases to close budget shortfalls. See Johnson, Collins, and Singham, 2010. 14. See Fisher, Johnson, and Smeeding, 2013a; 2013b. 15. See Thompson and Smeeding, 2013. They also adjust for household size, dividing income by the square root of the number of household members. 16. These ratios are for the top ends of the ninth and first deciles, the ninetieth percentile (P90), and the tenth percentile (P10). 17. Fisher, Johnson, and Smeeding, 2013b.
Additional Resources Congressional Budget Office. 2013. Trends in the Distribution of Household Income Between 1979 and 2010. Washington DC: U.S. Government Printing Office. DeNavas-Walt, Carmen, Bernadette D. Proctor, and Jessica C. Smith. 2013. Current Population Reports, P60-245. Income, Poverty, and Health Insurance Coverage in the United States: 2012. U.S. Government Printing Office. Washington, D.C. Fisher, Jonathan, David Johnson, and Timothy Smeeding, 2013a. “Measuring the Trends in Inequality of Individuals and Families: Income and Consumption,” American Economic Review: Papers & Proceedings, 103(3) 184-188.
Fisher, Jonathan, David Johnson, and Timothy Smeeding. 2013b. “Exploring the Divergence of Consumption and Income Inequality During the Great Recession” presented to the American Economic Association, Philadelphia, PA. January, 2014. Johnson, Nicholas, Catherine Collins, and Ashali Singham. 2010. “State Tax Changes in Response to the Recession,” Center on Budget and Policy Priorities, March 8, 2010: http://www.cbpp.org/cms/index. cfm?fa=view&id=3108 Larrimore, Jeff, Richard Burkhauser, and Philip Armour. 2013. “Accounting for Income Changes over the Great Recession (20072010) Relative to Previous Recessions: The Importance of Taxes and Transfers,” NBER Working Paper 19699, November, 2013.
Saez, Emmanuel. 2013. “Striking it Richer: The Evolution of Top Incomes in the United States (updated)”, Mimeo, September 2013. http://elsa.berkeley.edu/~saez/saez-UStopincomes-2012.pdf Smeeding, Timothy and Jeffrey Thompson. 2011. “Recent Trends in Income Inequality: Labor, Wealth and More Complete Measures of Income.” Research in Labor Economics. May: 1-49 Thompson, Jeffrey and Timothy Smeeding. 2013. “Inequality and Poverty in the United States: the Aftermath of the Great Recession,” Finance and Economics Discussion Series Working Paper 2013-51, Federal Reserve Board of Governors.
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wealth inequality
wealth inequality January 2014 The Stanford Center on Poverty and Inequality
B y E d wa r d N . W o l f f
Key findings • After two decades of robust growth in middle class wealth, median net worth plummeted by 47% from 2007 to 2010. • As median net worth declined during the Great Recession, wealth became more unequally distributed. In fact, wealth inequality rose for the first time since the early 1980s, even as income inequality declined (under some measures). • The recent sharp fall in median net worth as well as the rising inequality of net worth are due to the high leverage of middle class families and the high share of homes in their portfolio. • The Great Recession hit black households much harder than white households, with the ratio of net worth between the two groups falling from 0.19 in 2007 to 0.14 in 2010. Hispanic households were hammered even more by the Great Recession: The ratio of net worth between Hispanic and white households plummeted from 0.26 to 0.15.
T
he last three decades have witnessed some remarkable asset price movements. While the median house price in real terms was virtually the same in 1989 and 2001, house prices suddenly took off thereafter, rising 19 percent in real terms from 2001 to 2007. Then, the Great Recession hit and home prices plummeted 24 percent. This was followed by a partial recovery. Median house prices rose 7.8 percent through September 2013, still well below their 2007 value. The stock market has trended differently during this same period. In contrast to the housing market, the stock market boomed in the 1990s, surging 171 percent between 1989 and 2001. However, from 2001 to 2007, the Standard & Poor’s 500 was up only 6 percent. During the Great Recession, it nosedived 26 percent. In this case, there was a strong recovery after 2010, with stock prices up 41 percent through September 2013. This brief poses four simple questions in response to such shocks: How have the rapid and unprecedented movements in asset prices affected the absolute amount of middle class wealth? How have they affected wealth inequality? Which groups were most affected by these changes? And, finally, has the post-recession period brought about much of a recovery in household wealth? It will be shown that the Great Recession abruptly reversed a trend of robust growth in middle class wealth since the early 1980s and also brought about the first growth in wealth inequality since the early 1980s. Median wealth plummeted 47 percent from
2007 to 2010, and the inequality of net worth, after almost two decades of little movement, rose sharply. Relative indebtedness of the middle class also continued to expand, even though the middle class had stopped taking on new debt. What drove these changes? This brief will show that the recent sharp fall in median net worth and the recent rise in the inequality of net worth are traceable to the high leverage of middle class families and the high share of homes in their portfolios. Median net worth fell because middle class homeowners were not able to shed mortgage debt. At the same time, their home values declined. Wealth inequality increased because home values composed 67 percent of middle class wealth but only 9 percent of the portfolios of the wealthiest one percent. It follows that the wealthiest were better protected against the sharp decline in housing prices during the Great Recession. This brief will also reveal that the middle class wealth fallout was not felt equally across demographic groups. The sharp fall in the relative net worth of both minority and young households is again traceable to their high leverage and the high share of homes in their portfolio. The ratio of net worth between black and white households fell from 0.19 in 2007 to 0.14 in 2010 and that between Hispanic and white households plummeted from 0.26 to 0.15. The relative wealth of the under 35 age group (when compared to total wealth) plummeted from 0.17 in 2007 to 0.10 in 2010 and that of age group 35-44 from 0.58 to 0.41.
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wealth inequality
But has household wealth recovered since the Great Recession? The results are mixed. According to the Financial Accounts of the United States, mean household wealth fully recovered by the second quarter of 2013. Other sources, however, paint a less optimistic portrait. In the following sections, these key results are laid out and elaborated. The concluding section will then examine the forces behind these results. For the years 1983 to 2010, the primary data source is the Survey of Consumer Finances (SCF), conducted by the Federal Reserve Board. The Great Reversal in Wealth It is useful to begin by examining trends in mean and median household wealth. These trends evince what may be called the “great reversal” in which the relatively high rates of growth in recent decades come to a sudden end with the Great Recession. Figure 1 shows the robust growth in wealth from 1983 to 2007. Median wealth grew at 1.1 percent per year from 1983 to 1989, 1.3 percent per year between 1989 and 2001, and then at 2.9 percent per year from 2001 to 2007. Between 2007 and 2010, median wealth plunged by a staggering 47 percent. The primary reasons, as we shall see below, were the collapse in the housing market and the high leverage of middle class families.
tail” of the distribution, also grew vigorously over this time period. It grew at 2.3 percent per year from 1983 to 1989, at 3.0 percent per year from 1989 to 2001, and at 3.1 percent per year from 2001 to 2007. Between 1983 and 2007, mean wealth grew more than twice as fast as the median, indicating widening inequality of wealth over these years. The Great Recession also saw an absolute decline in mean household wealth. However, whereas median wealth plunged by 47 percent, mean wealth fell by only 18 percent. The relatively faster growth in mean wealth than median wealth from 2007 to 2010 was coincident with rising wealth inequality. The changes in the income distribution are rather different. When the Current Population Survey (CPS) is used to track median income in real terms, we see that it gained 11 percent between 1983 and 1989, grew by only 2.3 percent from 1989 to 2001, and then grew by another 1.6 percent from 2001 to 2007 (see Figure 2). From 2007 to 2010, it fell by 6.4 percent. This reduction was not nearly as great as that in median wealth. Mean income surged by 2.4 percent per year from 1983 to 1989, advanced by 0.9 percent per year from 1989 to 2001, and then dipped by 0.1 percent per year from 2001 to 2007. Mean income also dropped in real terms from 2007 to 2010, by 5.0 percent, slightly less than that of median income.
Mean net worth, which is more sensitive to the long “right
figure 1.
Mean and Median Net Worth, 1983-2010
figure 2.
600
Mean and Median Household Income, 1983-2010
80 70
500
60
1000s, 2010$
1000s, 2010$
400 300 200
50 40 30 20
100
10 0
0 1983
1989
1992
Median Net Worth Source: Survey of Consumer Finances.
1995
1998
2001
Mean Net Worth
2004
2007
2010
37
1983
1989
Median Income
1992
1995
Mean Income
Source: Survey of Consumer Finances.
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1998
2001
2004
2007
2010
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wealth inequality
The upshot is that the Great Recession was indeed a “great reversal” of what had been a long period of expansion in wealth. By contrast, the effects of the Great Recession on income were less profound, although here again it interrupted what had been a long period of increase (except that mean income was roughly stable from 2001 to 2007). Trends in Inequality What about trends in inequality? The Gini coefficient for wealth, after rising steeply between 1983 and 1989 from 0.80 to 0.83, remained virtually unchanged from 1989 to 2007 (Figure 3). In contrast, the years of the Great Recession saw a very sharp elevation in wealth inequality, with the Gini coefficient rising to 0.87. The time trend for income inequality contrasts with that for wealth inequality. Income inequality showed a sharp rise from 1983 to 1989, with the Gini coefficient expanding from 0.48 to 0.52, and again from 1989 to 2007, with the Gini index advancing to 0.57. Perhaps somewhat surprisingly, the Great Recession witnessed a rather sharp contraction in income inequality. The Gini coefficient fell from 0.57 in 2007 to 0.55 in 2010. One of the puzzles we have to contend with is that wealth inequality rose sharply over the Great Recession while income inequality contracted, at least according to the Survey of Consumer Finances used here. It should be noted, however, that other data sets and other measures of inequality do not suggest a sharp contraction (see, e.g., the brief on income inequality).
figure 3.
Wealth and Income Inequality, 1983-2010 (Gini Coefficients)
It is of course well known that wealth is more unequally distributed than income. This result is quite dramatically revealed in Figure 3. Because the Great Recession increased wealth inequality but reduced income inequality, this disparity has become even more pronounced in recent years. Portfolios and Debt It is also important to monitor portfolio composition because some types of assets, particularly housing assets, were especially vulnerable during the Great Recession. In 2010, homes accounted for 31 percent of total assets among all households (first column of Figure 4). However, net home equity—home value minus mortgage debt—amounted to only 18 percent of total assets. Liquid assets made up 6 percent and pension accounts 15 percent. “Investment assets” (non-home real estate, business equity, financial securities, corporate stock, mutual funds, and trust funds) collectively amounted to 45 percent. The debt-equity ratio (the ratio of debt to net worth) was 0.21 and the debt-income ratio was 1.27. There are marked differences in portfolio composition by wealth class. As shown in the second column of Figure 4, the wealthiest one percent invested over three quarters of their savings in investment assets. Housing accounted for only 9 percent, liquid assets 5 percent, and pension 8 percent. The debt-equity ratio was only 0.03, the debt-income ratio was 0.61, and the ratio of mortgage debt to house value was 0.19. In contrast, 67 percent of the assets of the middle three wealth quintiles was invested in their home, a crucial difference relative to the portfolios of the wealthier (column 3 of
figure 4.
0.900
160
0.850
140
0.800
Composition of Household Wealth by Wealth Class, 2010
120 100
0.700 Percent
gini coefficient
0.750
0.650 0.600
80 60
0.550
40
0.500
20
0.450 0.400
0 1983 1989 1992 1995 1998 2001 2004 2007 2010 Wealth Inequality
Home
Liquid Assets
Income Inequality
Pension Stocks & Businesses Accounts Securities & Real Estate
All Households Source: Survey of Consumer Finances.
Top 1%
Source: Survey of Consumer Finances.
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Debt/ Equity
Middle 3 Wealth Quintiles
Debt/ Income
wealth inequality Figure 4). Home equity amounted to only 32 percent of total assets, a reflection of their large mortgage debt. Another 20 percent went into monetary savings and pension accounts. Together housing, liquid, and pension assets accounted for 87 percent, with the remainder in investment assets. Their debt-equity ratio was 0.72 and their debt-income ratio was 1.35, both much higher than that of the top quintile. Finally, their mortgage debt amounted to a little more than half the value of their home. The rather staggering debt level of the middle class in 2010 raises the question of whether this was a recent phenomenon. It indeed was. There was a sharp rise in the debt-equity ratio of the middle class from 0.37 in 1983 to 0.61 in 2007, mainly a reflection of a steep rise in mortgage debt. The debt-income ratio more than doubled from 1983 to 2007, from 0.67 to 1.57. The rise in the debt-equity ratio and the debt to income ratio was much steeper than for all households. In 1983, the debtincome ratio was about the same for middle class as for all households, but by 2007 the ratio was much larger for the middle class. Then, the Great Recession hit. The debt-equity ratio continued to rise, reaching 0.72 in 2010, but there was actually a retrenchment in the debt-income ratio, falling to 1.35. The reason is that, from 2007 to 2010, the mean debt of the middle class actually contracted by 25 percent in constant dollars. Mortgage debt fell by 23 percent as families paid
figure 5.
down their outstanding balances, and other debt dropped by 32 percent as families paid off credit card balances and other consumer debt. The steep rise in the debt-equity ratio was due to the sharp drop in net worth, while the decline in the debt to income ratio was almost exclusively due to the sharp contraction of overall debt. The Role of Leverage Two major puzzles emerge. The first is the steep plunge in median net worth in real terms of 47 percent between 2007 and 2010 despite an only moderate drop in median income of 6.4 percent and less steep declines in housing and stock prices of 24 percent and 26 percent, respectively. The second is the steep increase of wealth inequality of 0.035 Gini points despite a decline in income inequality of 0.025 Gini points and a virtually unchanged ratio of stock to housing price. As noted above, wealth inequality is positively related to the ratio of stock to house prices, since stocks are heavily concentrated among the rich and real estate is the chief asset of the middle class. Changes in median wealth and wealth inequality from 2007 to 2010 can be explained by leverage, the ratio of debt to net worth. The steep fall in median wealth was due in large measure to the high leverage of middle class households. The spike in wealth inequality was largely due to differential leverage between the rich and the middle class.
Average Annual Rates of Return by Period and Wealth Class Gross Assets All Housholds
Gross Assets Top 1%
Gross Assets Middle 3 Quintiles
Net Worth All Housholds
Net Worth Top 1%
8 6 4
percent
2 0 -2 -4 -6 -8 -10 -12 -14 1983-1989
1989-2001
39
2001-2007
2007-2010
Source: Survey of Consumer Finances.
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Net Worth Middle 3 Quintiles
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wealth inequality
Figure 5 shows average annual real rates of return for both gross assets and net worth over the period from 1983 to 2010. Results are based on the average portfolio composition over the period. It is of interest to examine the results for all households. The overall annual return on gross assets rose from 2.20 percent in the 1983-1989 period to 3.25 percent in the 1989-2001 period and then to 3.34 percent in the 20012007 period before plummeting to -6.95 percent from 2007 to 2010. The average annual rate of return on net worth among all households also increased from 3.17 percent in the first period to 4.25 percent in the second and to 4.31 percent in the third but then fell off sharply to -7.98 percent in the last period. It is notable that the returns on net worth are uniformly higher— by about one percentage point—than those on gross assets over the first three periods, when asset prices were generally rising. However, in the 2007-2010 period, the opposite was the case, with the annual return on net worth 1.03 percentage points lower than that on gross assets. These results illustrate the effect of leverage, raising the return when asset prices rise and lowering the return when asset prices fall. Over the full 1983-2010 period, the annual return on net worth was 0.87 percentage points higher than that on gross assets. There are striking differences in returns by wealth class. The returns on gross assets were generally higher for the top one percent than the middle three quintiles. The differences are quite substantial. Over the full 1983-2010 period, the average annual rate of return on gross assets for the top one percent was 1.39 percentage points greater than that of the middle quintiles. The differences reflect the greater share of high yield investment assets like stocks in the portfolios of the rich and the greater share of housing in the portfolio of the middle class (see Figure 4). This pattern is almost exactly reversed for returns on net worth. In this case, in the first three periods, the return was higher for the middle quintiles (except for the 1983-1989 period when its return was slightly lower than that of the top one percent), but in the 2007-2010 period the middle three quintiles registered a lower (that is, more negative) return. Differences in returns between the top one percent and the middle quintiles were quite substantial in some years. In the 2001-2007 period, the annual return was 1.92 percentage points higher for the middle quintiles, while in the 2007-2010 period, it was 4.27 percentage points higher for the top percentile. The spread in returns between the top one percent and the middle quintiles reflects the much higher leverage of the middle class (see Figure 4).
The huge negative rate of return on net worth of the middle quintiles was largely responsible for the precipitous drop in median net worth between 2007 and 2010. This factor, in turn, was attributable to the steep drop in housing prices and the very high leverage of the middle class. Likewise, the very high rate of return on net worth of the middle three quintiles over the 2001-2007 period (almost 6.0 percent per year) played a big role in explaining the robust advance of median net worth, despite the sluggish growth in median income. This, in turn, was a result of their high leverage coupled with the boom in housing prices. The substantial differential in returns on net worth between the middle quintiles and the top percentile (over a point and a half lower) helps explain why wealth inequality rose sharply between 2007 and 2010 despite the decline in income inequality. Likewise this differential over the 2001-2007 period (a spread of about two percentage points in favor of the middle quintiles) helps account for the stasis in wealth inequality over these years despite the increase in income inequality. The Racial Divide Widens Striking differences are found in the wealth holdings of specific racial and ethnic groups. In Figure 6, households are divided into three groups: (i) non-Hispanic whites (“whites” for short), (ii) non-Hispanic African-Americans (“blacks” for short), and (iii) Hispanics. In 2007, while the ratio of mean incomes between black and white households was an already low 0.48, the ratio of mean wealth holdings was even lower, at 0.19. The homeownership rate for black households was 49 percent in 2007, a little less than two thirds that among whites. Between 1982 and 2006, while the average real income of white households increased by 42 percent, it rose by only 28 percent for black households. As a result, the ratio of mean income slipped from 0.54 to 0.48. Between 1983 and 2001, average net worth in constant dollars climbed by 73 percent for white households but rose by only 31 percent for black households, so that the net worth ratio fell from 0.19 to 0.14. However, between 2001 and 2007, mean net worth among blacks gained an astounding 58 percent while white wealth advanced by 29 percent, so that by 2007 the net worth ratio was back to 0.19, the same level as in 1983. The large gains made by black households over these six years can be traced to the much higher share of homes in their portfolio (46 percent of total assets in 2001, compared to 27 percent among whites). The homeownership rate of black households grew from 44 to 49 percent between 1983 and 2007.
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wealth inequality The picture is rather different for Hispanics. The ratio of mean income between Hispanics and whites in 2007 was 0.50, almost the same as that between black and white households. The ratio of mean net worth was 0.26 compared to a ratio of 0.19 between blacks and whites. The Hispanic homeownership rate was 49 percent, almost identical to that of black households. Over the years 1983 to 2007, Hispanic mean income grew by only 18 percent, so that the ratio of Hispanic to white mean income slid from 0.60 to 0.50. On the other hand, between 1983 and 2001, mean wealth doubled for Hispanic households, at a slightly higher rate than whites, so the ratio of mean net worth increased slightly from 0.16 to 0.17. Mean net worth among Hispanics then climbed by another 82 percent between 2001 and 2007, and the corresponding ratio advanced to 0.26, quite a bit higher than that between black and white households. The surge in Hispanic wealth from 2001 to 2007 can be traced to a five percentage point jump in the Hispanic home ownership rate. The racial picture changed radically by 2010. While the ratio of mean income between black and white households changed very little between 2007 and 2010 (income fell for both groups), the ratio of mean net worth dropped from 0.19 to 0.14. The proximate causes were the higher leverage of black households and their higher share of housing wealth in gross assets. In 2007, the debt-equity ratio among blacks was an astounding 0.55, compared to 0.15 among whites,
figure 6.
Ratio of Mean Net Worth by Race and Ethnicity, 1983-2010
while housing as a share of gross assets was 0.54 for the former as against 0.31 for the latter. The sharp drop in home prices from 2007 to 2010 thus led to a relatively steeper loss in home equity for blacks (25 percent) than for whites (21 percent), and this factor, in turn, led to a much steeper fall in mean net worth for black households than white households. The Great Recession actually hit Hispanic households much harder than blacks in terms of wealth. Mean income among Hispanic households rose a bit from 2007 to 2010, and the ratio with respect to white households increased from 0.50 to 0.57. However, the mean net worth in 2010 dollars of Hispanics fell almost in half, so that the mean wealth ratio relative to whites plummeted from 0.26 to 0.15. The same factors were responsible as in the case of black households. In 2007, the debt-equity ratio for Hispanics was 0.51, compared to 0.15 among whites, while housing as a share of gross assets was 0.53 for the former as against 0.31 for the latter. As a result, net home equity dropped by 48 percent among Hispanic home owners, compared to 21 percent among white home owners, and this factor, in turn, was largely responsible for the huge decline in Hispanic net worth both in absolute and relative terms. Wealth Shifts from the Young to the Old There were also notable shifts in relative wealth holdings by age group between 1983 and 2007 (see Figure 7). The relative wealth of the youngest age group, under 35 years of age, declined from 21 percent of the overall mean in 1983 to
Ratio of Mean Net Worth of Young Age Groups to Overall Mean Net Worth, 1983-2010
figure 7.
.30
.75 .65 .55
ratio
ratio
.25
.20
.45 .35 .25
.15
.15 .10
1983 1989 1992 1995 1998 2001 2004 2007 2010 Black/White
Source: Survey of Consumer Finances.
Hispanic/White
41
.05
1983 1989 1992 1995 1998 2001 2004 2007 2010 Under 35
Source: Survey of Consumer Finances.
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35-44
42
wealth inequality
17 percent in 2007. In 2007, the mean wealth of the youngest age group was $95,900 (in 2010 dollars), which was only slightly more than the mean wealth of this age group in 1989. The mean net worth of the 35-44 age group collapsed from 0.71 relative to the overall mean in 1983 to 0.58 in 2007. Changes in relative wealth were even more dramatic during the period from 2007 to 2010. The relative wealth of the under 35 age group plummeted from 0.17 to 0.10 and that of age group 35-44 from 0.58 to 0.41. In 2010 dollar terms, the average wealth of the youngest age group collapsed from $95,500 in 2007 to $48,400 in 2010, while that of age group 35-44 shrank from $325,000 to $190,000. Changes in the relative wealth position of the younger age groups over the Great Recession can be explained by their higher debt-equity ratio and the heavier concentration of homes in their portfolio. Homes comprised over half the value of total assets for the age group 35 and under in 2007, and the share tended to fall off with age. There was also a pronounced fall off of the debt-equity ratio with age, declining from 0.93 for the youngest group to 0.02 for the oldest, while the debt-income ratio for these groups declined from 1.68 to 0.30. Younger households were thus more heavily invested in homes and more heavily in debt, whereas the portfolio of older households was more heavily skewed to financial assets. As such, the wealth position of younger households was hit much harder by the Great Recession than that for older households.
Mean Household Wealth (from the Financial Accounts of the United States, 2013$)
figure 8.
400
The steep decline in house prices from 2007 to 2010 thus led to a much more pronounced loss in home equity for the youngest age group (59 percent) than for all households (26 percent), and this factor, in turn, led to a much steeper fall in their net worth. The story is very similar for age group 35 to 44. Their debt-equity ratio was 0.41 in 2007, and their share of housing in gross assets was 0.44, both much higher than the corresponding figures for all households. As with the youngest age group, the drop in home prices from 2007 to 2010 caused a large fall in home equity of 49 percent, which in turn caused a steep collapse in their net worth. Recovery on the Horizon? What, if anything, can be concluded about trends in wealth after 2010? The results presented to this point have been based on the Survey of Consumer Finance, but these data are not available after 2010. This section reports briefly on four other sources that may be used to assess post-2010 trends. The first is the Survey of Income and Program Participation (SIPP), conducted annually by the U.S. Bureau of the Census. It shows essentially no change in median household wealth in real terms from 2010 to 2011. In contrast, wealth data from the Panel Study of Income Dynamics (PSID) show a continued plunge in median net worth. A third source, based on the Consumer Finance Monthly, shows a still different result. According to Lucia Dunn and Randall Olsen, median net worth in real terms hit its low point in 2010 but then more than doubled (a gain of 115 percent) through the first half of 2013. Real mean household wealth, in contrast, reached its nadir in 2009 and subsequently increased by 58 percent through the first half of 2013. In both cases, net worth in 2013 was still below its peak value in 2006 (with the median 30 percent below and the mean 14 percent below). The fourth source is the Financial Accounts of the United States (which used to be called the “Flow of Funds”). This source differs from the other three in that it is based on aggregate data instead of household survey data. Results on mean household wealth in 2013 dollars based on my own calculations are shown in Figure 8. The figure indicates a peak wealth figure of $387,000 in the first quarter of 2008. This was followed by a pronounced fall of 24 percent to its lowest value of $294,000 reached in the first quarter of 2009. Mean household wealth then started to increase as asset markets recovered and reached a figure of $386,000 by the second quarter of 2013, just about equal to its previous high.
thousands $
350
300
250 2003-05
2007
2009
2011
2013
Source: Survey of Consumer Finances.
national report card • The Stanford Center on Poverty and Inequality
wealth inequality The unfortunate upshot: The results are mixed. Because conclusions differ across sources, it is probably best to withhold judgment at this point. The next SCF is expected to be released in late 2014. Conclusions Median wealth showed robust growth during the 1980s and 1990s and an even faster advance from 2001 to 2007. However, from 2007 to 2010, house prices fell by 24 percent in real terms, stock prices by 26 percent, and median wealth by a staggering 47 percent. Wealth inequality, after remaining relatively stable from 1989 to 2007, also showed a steep increase over the Great Recession, with the Gini coefficient climbing from 0.834 to 0.870. The key to understanding the plight of the middle three wealth quintiles over the Great Recession was their high degree of leverage and the high concentration of assets in their home. The steep decline in median net worth between 2007 and 2010 was primarily due to their very high negative return on net worth (-8.9 percent per year). This, in turn, was attributable to their very high degree of leverage and the precipitous fall in home prices. High leverage, moreover, helps explain why median wealth fell more than house (and stock) prices over these years and declined much more than median household income. The large spread in rates of return on net worth between the middle three wealth quintiles and the top percentile (over a point and a half lower) also largely explains why wealth inequality increased steeply from 2007 to 2010 despite the 0.025 Gini point decline in income inequality. Indeed, the middle class took a bigger relative hit on their net worth from the decline in home prices than the top 20 percent did from the stock market plunge, a result that has not been widely appreciated.
43
The racial disparity in wealth holdings was almost exactly the same in 2007 as in 1983. However, the Great Recession hit black households much harder than whites. Black households suffered substantial relative (and absolute) losses from 2007 to 2010 because they had a higher share of assets invested in the home than did whites and a much higher debt-equity ratio (0.55 versus 0.15). Hispanic households made sizeable gains on whites from 1983 to 2007. However, in a reversal of fortune, Hispanic households got hammered by the Great Recession. The relative (and absolute) losses suffered by Hispanic households over these three years are likewise traceable to the much larger share of assets invested in the home and a much higher debt-equity ratio (0.51 versus 0.15). Young households also got pummeled by the Great Recession. The same two factors explain the losses suffered by young households—the higher share of homes in their wealth portfolio and their much higher leverage ratios. Results are mixed on whether household wealth has turned around since the Great Recession. The SIPP data show no change through 2011, and the PSID data show a continued fall, also through 2011. Data from the Consumer Finance Monthly, in contrast, shows a recovery from its bottom point, but net worth in 2013 was still below its previous high. In contrast, data from the Financial Accounts of the United States indicate a full recovery in mean household wealth by the second quarter of 2013. We therefore have to await the release of the next SCF, slated for late 2014, to reach any definitive conclusion on recent trends in household wealth. Whatever the results may be, we are obviously in the midst of a very volatile period, one of those rare moments of rapid and momentous change. ■
Additional Resources Dunn, Lucia, and Randall Olsen, “U.S. Household Real Net Worth through the Great Recession and beyond: Have We Recovered?” Economic Letters, forthcoming.
Wolff, Edward N. Top Heavy: A Study of Increasing Inequality of Wealth in America. Newly updated and expanded edition, New York: the New Press, 2002.
Pfeffer, Fabian T., Sheldon Danziger, and Robert F. Schoeni, “Wealth Disparities before and after the Great Recession,” National Poverty Center Working Paper #13-05. University of Michigan, April 2013.
Wolff, Edward N., Ajit Zacharias, and Thomas Masterson. 2009. “Long-Term Trends in the Levy Institute Measure of Economic Well-Being (LIMEW), United States, 1959-2004,” Working Paper No. 556, Levy Economics Institute of Bard College, Annandale-on-Hudson, NY.
national report card • The Stanford Center on Poverty and Inequality
national report card
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health inequality
health inequality January 2014 The Stanford Center on Poverty and Inequality
B y S a r a h A . B u r g a r d a n d M o l ly M . K i n g
• Although there is improvement in some key health indicators, there is also moderate deterioration in others. For example, 9.8 percent of Americans reported that they were in poor or fair health in 2012, an increase of 0.6 percentage points since 1997. • Economic, racial, and ethnic disparities in health outcomes are often substantial and are sometimes increasing. The proportion of Blacks and Hispanics, for example, who could not afford necessary care rose at a faster rate during the Great Recession than did the corresponding (and far lower) proportion for Whites. • Since 2000, the proportion of Americans who have any health insurance coverage has declined (to 84.6 percent in 2012), although there has been a slight reversal in this general pattern of decline since 2010. The proportion of children who are insured has increased during this same period and is now at the very highest level since 2000.
T
The key backdrop to this assessment is the tripling of U.S. health expenditures since the
1960s. In 2012, per capita expenditures on health were $8,915, more than double those from 1995, though growth has slowed in the past 4 years.1 Some of this rise is attributable to population aging. Costs associated with Medicare, a program established in 1965 to subsidize health care for those aged 65 and older, have grown as the elderly population constitutes an ever-larger portion of the U.S. population. Still, overall U.S. health expenditures have increased faster than the growth of the elderly population and faster than health expenditures in other OECD countries.2 It is possible that such rising costs have led to a more unequal distribution of health and health care. At the same time, health inequalities may also be affected by the
figure 1. Additional Years of Life Expectancy at Age 65 for Men Covered by Social Security, by Year and Lifetime Earnings Group.
23 years of remaining life expectancy at age 65
Key findings
here are many reasons why poverty matters, but it is especially troubling that it affects such fundamental outcomes as health and access to health care. If poverty did not bring about all manner of health risks, we would likely be somewhat less troubled by it. But of course poverty and other forms of social and economic disadvantage do often translate into deficits in health and health care. The purpose of this brief is to examine long-term trends in American health and to lay out the current state of evidence on the extent to which health and health care are unequally distributed. We also note how the recent economic downturn affected these trends and disparities.
22 21 20 19 18 17 16 15 14 1975
1980
1985
Earnings in Top Half of Distribution
1990
1995
2000
Earnings in Bottom Half of Distribution
Source: SSA Working papers.4
national report card • The Stanford Center on Poverty and Inequality
2005
2012
health inequality
economy (e.g., recessions), changes in how insurance is provided, and any number of other factors. In this brief, our objective is not to attempt to tease out the causes of any possible changes in health inequalities, but rather to provide a descriptive summary of the current evidence on trends in (a) health, (b) foregone health care and insurance coverage, and (c) health risk factors. To preview our results, we find first that some health indicators, such as life expectancy, show an overall improvement. But not all indicators are improving. For example, an increasing number of Americans report delaying or foregoing health care, particularly during the recent economic recession. Second, economic and racial disparities in health indicators are often substantial, and when changes in these disparities are observed, they usually take the form of an increase in absolute size. Third, a large proportion of Americans still remain uninsured in 2012 (i.e., 15 percent), although the proportion of children who are uninsured declined by nearly 2 percentage points between the late 1990s and 2012. Trends in Health We lead off our brief by presenting trends in life expectancy, physical health status, and mental health status. To the extent
Percentage of People Reporting Poor or Fair Health, by Poverty Level Status, 1997-2012.
figure 2.
45
possible given available data, we focus on the degree to which such outcomes are unequally distributed. Life Expectancy
Life expectancy, one of the most basic measures of population health, has increased substantially since 1960. For U.S. males, life expectancy at birth rose by almost ten years since 1960, to 76 years as of 2011. Females started from a higher baseline, but still experienced an eight-year increase in life expectancy since 1960. Females born in 2011 could expect to live to age 81 on average.3 This overall improvement in life expectancy masks a troubling trend toward growing income inequality in life expectancy. The amount of inequality was once quite limited: Among men born in 1912 (who reached age 65 in 1977), those with above-median earnings during their careers could expect to live an additional 15.5 years, whereas those with below-median earnings could expect to live an additional 15.0 years (see Figure 1). The penalty to being poorer was thus but a half-year in life expectancy. A far more substantial disparity opened over the next thirty years. By 2006, the average life expectancy of 65 year-old men was 5.5 years longer for above-median earners than belowmedian earners. It follows that approximately 6/7ths of the overall improvement in menâ&#x20AC;&#x2122;s life expectancy (at age 65) dur-
Percentage of Children who Currently Have Asthma by Race and Hispanic Origin, 2001-2012.
figure 3.
18
30
16 25 14 12 percent
percent
20
15
10 8 6
10
4 5 2 0
0 1998
Under 100%
2000
2002
100%-200%
2004
2006
2008
200%-400%
Source: National Health Interview Study (https://www.ihis.us/ihis/).
2010
2012
400% or more
2002
2004
2006
2008
Black, non-Hispanic
White, non-Hispanic
Hispanic
Asian
Source: National Health Interview Study (https://www.ihis.us/ihis/).
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2010
2012
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health inequality
ing this 30 year period accrued to those with above-median earnings. Health Status
Although life expectancy is a key indicator of health, other measures of health status speak more directly to the quality of life. We first present omnibus trends in self-reported health status and then shift to a measure of asthma as one of the key health indicators for children. Using data from the National Health Interview Study, we find that 9.8 percent of Americans reported that they were in poor or fair health in 2012, an increase of 0.6 percentage points since 1997 (not shown). As shown in Figure 2, there are wide and significant income disparities in health status in 2012, with those in poverty (i.e., those whose income is less than 100% of that year’s poverty threshold) over five times more likely to report poor or fair health than those with incomes at least four times the poverty threshold (i.e., 26.2/4.8= 5.4). Although the disparities are wide, there is no strong evidence here of growing disparities by income since 1997. A key health indicator for children is the asthma rate. It is a dangerous condition; it is costly in terms of lost work for caregivers; and it can lead to prolonged school disruptions
Percentage of Adults Age 18 and Over who Experienced Serious Psychological Distress During the Past 30 Days by Poverty Level, 1997-2012.
for children. In this case, racial disparities in asthma are especially troubling, and we therefore present those in Figure 3 (again drawing on the National Health Interview Study). As shown here, there was a sharp uptick in 2006-2009—in the Great Recession period—in the proportion of African American children with asthma. Fortunately, that increase has now leveled out in the recovery period, albeit without fully returning to pre-recession levels. In contrast, rates among Hispanic and White non-Hispanic children have remained relatively steady. Although rates among Asian children showed large increases in 2004-2005 and 2008-2009, those high levels subsequently declined back to near the original rates. The key change over the period shown in Figure 3 is thus a substantial rise in asthma rates for African American children; indeed they are now twice as likely as White children to have asthma. Mental Health Status
Many observers have been carefully following the mental health of Americans in the Great Recession era, as research has showed that suicide, unlike most other health indicators, was affected by earlier recessions and the Great Depression.5 In the National Health Interview Study, serious psychological distress is indexed by how often in the past 30 days individuals felt hopeless, nervous, restless, sad, worthless, or that “everything was an effort.” In 2011, psychological distress
Percentage of Adults, Children, and all People with Health Insurance Coverage in The United States, 1999-2012.
figure 4.
figure 5.
10
100
9 95
8 7
90 percent
percent
6 5
85
4 3
80
2 1
75
0 1998
Under 100%
2000
2002
100%-200%
2004
2006
2008
200%-400%
Source: National Health Interview Study (https://www.ihis.us/ihis/).
2010
2012
400% or more
2002
Under Age 18
2004
2006
All
2008
2010
Adults Only
Source: US Census Bureau (http://www.census.gov/hhes/www/hlthins/index.html).
national report card • The Stanford Center on Poverty and Inequality
2012
health inequality reached the highest levels in over a decade, with 3.4% of adults reporting serious psychological distress in the past 30 days. This level of distress then declined significantly in 2012 and is back on par with pre-recession levels. There are also significant income disparities in the rates of experiencing serious psychological distress (see Figure 4). During the period examined here, those living below the poverty level experienced approximately six to eight times the rate of psychological distress as those living at 400% or more of the poverty level. Adults living with incomes below the poverty line also experienced a much greater spike in psychological distress during the Great Recession, but the distress level for this income category tends to fluctuate more across time in general. Those with incomes between one and two times the poverty level experienced, on average, four times the levels of psychological distress as those in the highest income category. While the causal order between mental health and income is complex, these findings are significant and consistent with previous findings of correlations between lowered household incomes and the prevalence of mood disorders.6 Trends in Health Care Access Over the last two decades, both insurance premiums and out-of-pocket health costs have risen,7 and it is therefore
Uninsured Rates by Real Household Income (in 2012 dollars), 1999-2012.
important to track trends in insurance coverage and foregone health care. We provide key indicators of both outcomes here. Insurance Coverage
Despite continued growth in health expenditures, the proportion of Americans who have any health insurance coverage has declined since 1999, although there have been slight countervailing increases in this proportion since 2010 (see Figure 5). The latest available data, pertaining to 2012, indicate that slightly less than 85% of all Americans are insured. However, the proportion of children who are insured has increased by over 3 percentage points between the late 1990s and 2012, in part due to increased coverage by the taxpayer-funded Children’s Health Insurance Program (CHIP) established in 1997. We are likely to see a rise in coverage for all Americans with the implementation of the Affordable Care Act’s (ACA) individual mandate. These rates of health insurance coverage differ by household income (Figure 6). In 2012, nearly one quarter of those living in households with incomes of less than $25,000 were uninsured. The uninsured rate for those earning $75,000 and over in 2012 was only about one-third as high (i.e., 7.9%). Among those in the second-highest income category—households earning $50,000-$74,999 per year—15.0% were uninsured in
Percentage of Firms Offering Health Benefits, by Firm Wage Characteristics, 2013.
figure 6.
figure 7.
30
70 60%
25 50
percent
20
15
30 23%
10 10 5
0
0 1998
2000
2002
2004
2006
Less Than $25,000
$25,000–$49,999
$50,000–$74,999
$75,000 and Over
Source: US Census Bureau.8
2008
2010
2012
47
Less Than 35% Earn $23,000 a Year or Less
35% or More Earn $23,000 a Year or Less
Source: Kaiser/HRET Survey of Employer-Sponsored Health Benefits.9
national report card • The Stanford Center on Poverty and Inequality
48
health inequality
2012. We may see some changes in these disparities with the implementation of the Affordable Care Act’s subsidy for health insurance beginning in 2014. The tight coupling of health insurance coverage to employment in the United States has played a major role in exacerbating this inequality in health coverage. As shown in Figure 7, only 23 percent of firms with many low-wage workers offer health benefits, whereas 60 percent of firms with few low-wage workers offer health benefits. Foregone Care
Lacking health insurance coverage—or having inadequate insurance—can make needed health care unaffordable. While care may be foregone for a variety of reasons, Figure 8 illustrates the increase over the last decade and a half in the proportion of U.S. adults who reported either that they delayed medical care due to cost (upper line) or that they needed but could not afford medical care and had to forego it (lower line). The Great Recession saw a large spike in delayed care and a smaller increase in foregone needed care. Though levels of delayed or foregone care have decreased with the economic recovery, they are still higher than pre-recession levels. As the cost of health care continues to increase, these
Percentage of Adults in the United States who Delayed or Could not Afford Medical Care Due to Cost, 1998-2012.
This increase in foregone care has played out differently across subpopulations. Figure 9 again shows the proportion of adults who needed but could not afford medical care, but now separately by major racial and ethnic categories. The proportion of Blacks and Hispanics who could not afford needed care rose by over one third and one quarter, respectively, during the Great Recession, while the corresponding proportion rose by less than one sixth for White Americans. The proportion of Asian adults who report foregoing care due to cost has oscillated but remained around 4% for the last decade. These trends imply an increase in the absolute size of the racial and ethnic disparities in foregone care. As Figure 9 shows, the four groups were bunched more closely in 1999 than in 2012, with the only exception to this overall trend being a possible narrowing of the Black-Hispanic gap. There has also been an increase in the absolute magnitude of the income gap in care foregone due to cost (Figure 10). The
Percentage of Adults in the United States who Could not Afford Medical Care Due to Cost by Major Racial/Ethnic Category, 1999-2012.
figure 9.
14
14
12
12
10
10
8
8
percent
percent
figure 8.
trends suggest that the secular rise in needed but unaffordable care could resume. The changes brought about by the ACA could, on the other hand, buffer against such a trend for at least some kinds of health care.
6
6
4
4
2
2
0
0 1998
2000
2002
Medical Care Delayed Due to Cost, Past 12 Months
2004
2006
2008
2010
Needed but Could Not Afford Medical Care, Past 12 Months
Source: National Health Interview Study (https://www.ihis.us/ihis/).
2012
2000
Asian
2002
2004
Black
2006
Hispanic
2008
White
Source: National Health Interview Study (https://www.ihis.us/ihis/).
national report card • The Stanford Center on Poverty and Inequality
2010
2012
health inequality gap between the lowest and highest income groups was 12.7 percentage points in 1997, but it grew to 14.1 percentage points in 2012. Likewise, the gap between the second-poorest and the highest income groups grew from 8.9 percentage points in 1997 to 13.2 percentage points in 2012. For the time series pertaining to delayed care (Figure 11), the absolute gap between the lowest and highest income groups likewise increased, albeit again only slightly. We next ask whether there are particular types of medical care that are increasingly likely to be foregone as medical care costs rise or economic conditions worsen. As Figure 12 shows, there was an especially dramatic increase in foregone dental care, prescription eyeglasses, and prescription medications during the Great Recession. The recovery has, however, reversed the trend lines: the proportions of adults foregoing mental health care and prescription medications have now dipped below pre-recession values, while the proportions foregoing dental care and eyeglass purchases have declined but remain nearly a percentage point higher than they were before the recession. When income disparities in foregone care are examined, the evidence suggests in most cases a widening gap between those living below the poverty level and those with incomes
Percentage of Adults in the United States who did not Receive Medical Care (Foregone Care) Due to Cost by Poverty Level Category, 1997-2012.
figure 10.
49
of 400% or more of the poverty line. The mental health trends shown in Figure 13 are an example of such rising income disparities in care foregone for cost. Trends in Health Risk Factors We conclude by considering two health risk factors, heavy alcohol use and smoking, that have long been viewed as especially important. Although the relevant time series are quite noisy, these trends are nonetheless important enough to monitor. In Figure 14, we examine rates of heavy alcohol use, again with a breakdown by income group. The disparities assume the expected direction, with heavy drinking especially high within the poverty group. Over the last 15 years, there has been a downturn in heavy drinking among the well-off group (from 3.8 percent in 1997 to 2.9 percent in 2012), but there has not been any similar long-term trend among the poor group. The times series for cigarette smoking is less noisy and displays a clearer decline for all groups (Figure 15). As with drinking, the income disparities are substantial, with a slightly larger decline within the well-off group than within the poor group.
figure 11. Percentage of Adults in the United States who Delayed Medical Care Due to Cost by Poverty Level Category, 1997-2012.
20
25
18 16
20
14 15 percent
percent
12 10 8
10
6 4
5
2 0
0 1998
Under 100%
2000
2002
100%-200%
2004
2006
2008
200%-400%
Source: National Health Interview Study (https://www.ihis.us/ihis/).
2010
2012
400% and Above
1998
Under 100%
2000
2002
100%-200%
2004
2006
200%-400%
Source: National Health Interview Study (https://www.ihis.us/ihis/).
national report card â&#x20AC;˘ The Stanford Center on Poverty and Inequality
2008
2010
2012
400% and Above
50
health inequality
The Prognosis The health outlook in the United States is mixed. While some indicators of well-being are showing continued secular improvement, such as life expectancy, others are more worrisome, such as the rise in foregone and delayed medical care over the past decade and a half. Although aggregate health spending continues to increase, health insurance coverage among adults overall has slowly fallen over the past decade and a half (but coverage for children has increased). Moreover, average trends also disguise important social disparities, indeed most indicators show substantially worse standing for those in poverty, and some of these income gaps have grown in recent decades. In the trends we explored, we found that Americans weathered the Great Recession fairly well, with no decline in life expectancy or overall self-rated health. We observed continued secular trends toward better health behaviors even over the period of the Great Recession. For the most part, U.S. adults have not turned to damaging health behaviors to cope with the stresses of the recent downturn, and levels of psychological distress have returned to prerecession levels
Percentage of Adults in the United States who Needed but Could not Afford Different Types of Medical Care, 1998-2012
figure 12.
after a spike. The recession was, however, associated with some troubling trends, such as a growing racial gap in asthma between African American and non-Hispanic white children. Moreover, recessionary spikes have failed to entirely resolve for some types of foregone health care, and income-based disparities in foregone health care appear to have grown over the past decade and a half. The longer-term prognosis for health and health disparities is deeply tied to policy. The implementation of the individual mandate component of the Affordable Care Act in January 2014 introduces substantial changes to the health care access landscape. This raises important questions about what will happen with respect to coverage levels for people of different age groups, racial or ethnic groups, genders, and socioeconomic positions. It is unclear whether and how this increased access to health care will be reflected in levels of population health, given the tenuous link between access to medical care and actual health outcomes.11 Some health outcomes and disparities may be influenced by medical care but may also need to be addressed through public health or other initiatives.
Percentage of Adults in the United States who Needed but Could not Afford Mental Health Care by Poverty Level Category, 1997-2012.
figure 13.
18
7
16
6
14 5
10
percent
percent
12
8 6
4 3 2
4 1
2 0
0 1998
2000
2002
2004
Dental
Medical
Eyeglasses
Mental Health
2006
2008
2010
Prescription Medications
Source: National Health Interview Study (https://www.ihis.us/ihis/).
2012
1998
Under 100%
2000
2002
100%-200%
2004
2006
200%-400%
Source: National Health Interview Study (https://www.ihis.us/ihis/).
national report card â&#x20AC;˘ The Stanford Center on Poverty and Inequality
2008
2010
2012
400% and Above
health inequality
51
While it may not eliminate longstanding disparities, recent evidence does suggest that providing continuous health insurance coverage is a good place to start in supporting health care access. A gap in health insurance coverage increases the likelihood of foregoing care for individuals of all poverty levels. Among those with a gap in insurance coverage, individuals with family incomes below twice the poverty level are three times as likely to forego care, and those with
family incomes above twice the poverty level are four times as likely to forego care, compared with individuals with continuous coverage. Although these findings suggest that the Affordable Care Act may have important effects, it is important to remember that health is also responsive to a variety of social and environmental factors, including employment, income, housing security, and the quality of neighborhoods, schools, and workplaces. â&#x2013;
Heavy Alcohol Use in the Past Month Among Adults by Poverty Level Category, 1997-2012. (Note: Heavy alcohol use is defined as drinking five or more drinks on the same occasion on each of more than 60 days in the past year.)
figure 15.
figure 14.
Cigarette Smoking in the Past Month Among Adults by Poverty Level Category, 1997-2012.10
12
35 30
10
25
percent
percent
8
6
4
20 15 10
2
5
0
0 1998
Under 100%
2000
2002
100%-200%
2004
2006
2008
200%-400%
Source: National Health Interview Study (https://www.ihis.us/ihis/).
2010
2012
400% and Above
1998
Under 100%
2000
2002
100%-200%
2004
2006
200%-400%
Source: National Health Interview Study (https://www.ihis.us/ihis/).
national report card â&#x20AC;˘ The Stanford Center on Poverty and Inequality
2008
2010
2012
400% and Above
52
health
Notes 1. See Martin et al., 2014. 2. See OECD, 2013. 3. See OECD, 2013. 4. See Waldron, 2007. 5. See Tapia Granados & Diez Roux, 2009. 6. See Sareen, Afifi, McMillan, & Asmundson, 2011. 7. See Claxton et al., 2013, and OECD, 2013.
8. Estimates of uninsured rates “reflect the results of follow-up verification questions, which were asked of people who responded “no” to all questions about specific types of health insurance coverage in order to verify whether they were actually uninsured.” See DeNavasWalt, Proctor, & Smith, 2013: 28. 9. See Claxton et al., 2013: 40. 10. From 1997-2003, the figure represents adults age 18+ who have ever smoked 100 cigarettes and who currently smoke every day, currently smoke some days, or whose current
smoking status is unknown but who smoked at least one day or an unknown amount of days in the past 30 days. From 2004-2012, the figure represents the same except excludes those whose smoking status is unknown. (Source: IHIS codebook for variable “CIGSDAY,” is available: https://www.ihis.us/ihisaction/variables/CIGSDAY#universe_section). 11. See Newhouse, 1993.
Additional Resources Burgard, S. A. (2012). Is the Recession Making Us Sick? Pathways, 19–23. Burgard, S. A., Ailshire, J. A., & Kalousova, L. (2013). The Great Recession and Health: People, Populations, and Disparities. The ANNALS of the American Academy of Political and Social Science, 650(1), 194–213. doi:10.1177/0002716213500212 Burgard, S. A., & Hawkins, J. M. (2013). Race/Ethnicity, Educational Attainment, and Foregone Health Care in the United States in the 2007–2009 Recession. American Journal of Public Health, 1–7. doi:10.2105/ AJPH.2013.301512 Burgard, S. A., & King, M. M. (2014). Foregone Health Care & Psychological Wellbeing in the Wake of the Great Recession. Pathways, forthcoming. Centers for Disease Control and Prevention (2010). Vital signs: health insurance coverage and health care utilization—United States, 2006–2009 and January-March 2010. MMWR. Morbidity and mortality weekly report, 59(44), 1448–54. Retrieved from http://www.ncbi.nlm. nih.gov/pubmed/21063276
Claxton, G., Rae, M., Panchal, N., Damico, A., Kenward, K., & Whitmore, H. (2013). Employer Health Benefits 2013 Annual Survey. Retrieved from http://kff.org/private-insurance/ report/2013-employer-health-benefits/. Denavas-Walt, C., Proctor, B. D., & Smith, J. C. (2013). Income, Poverty, and Health Insurance Coverage in the United States: 2012 Current Population Reports. Retrieved from http:// www.census.gov/prod/2013pubs/p60-245.pdf Gallo, W. T., Teng, H. M., Falba, T. a, Kasl, S. V, Krumholz, H. M., & Bradley, E. H. (2006). The impact of late career job loss on myocardial infarction and stroke: a 10 year follow up using the health and retirement survey. Occupational and environmental medicine, 63(10), 683–7. doi:10.1136/oem.2006.026823 Martin, A. B., Hartman, M., Whittle, L., & Catlin, A. (2014). National Health Spending in 2012: Rate Of Health Spending Growth Remained Low For The Fourth Consecutive Year. Health Affairs, 33(1), 67–77. doi:10.1377/ hlthaff.2013.1254 Newhouse, J. P. (1993). Free for All? Lessons from the RAND Health Insurance Experiment. Cambridge, MA: Harvard University Press.
Sareen, J., Afifi, T. O., McMillan, K. A., & Asmundson, G. J. G. (2011). Relationship Between Household Income and Mental Disorders: Findings From a Population-Based Longitudinal Study. Arch Gen Psychiatry, 68(4), 419–427. Strully, K. W. (2009). Job loss and health in the U.S. labor market. Demography, 46(2), 221–46. doi:10.1353/dem.0.0050 Sullivan, D., & von Wachter, T. (2009). Job Displacement and Mortality: An Analysis Using Administrative Data*. The Quarterly Journal of Economics, (August), 1265–1306. Tapia Granados, J. A., & Diez Roux, A. V. (2009). Life and death during the Great Depression. Journal of Health Economics, 106(41), 17290–5. doi:10.1073/ pnas.0904491106 Waldron, H. (2007). Trends in Mortality Differentials and Life Expectancy for Male Social Security – Covered Workers, by Average Relative Earnings. ORES Working Paper Series, (108). Retrieved from http://www.ssa.gov/ policy/docs/workingpapers/wp108.html.
OECD. (2013). OECD Health Data. Retrieved from http://www.compareyourcountry.org/ health/health-spending-gdp?cr=usa&lg=en
national report card • The Stanford Center on Poverty and Inequality
national report card
education
education
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January 2014 The Stanford Center on Poverty and Inequality
B y S e a n F. R e a r d o n
Key findings • White-black and white-Hispanic academic achievements gaps have narrowed by roughly 40% in the last four decades, and continue to narrow today in most states, although slowly. Nonetheless, these achievement gaps remain very large. • The achievement gap between children from highand low-income families has widened by roughly 40% in the last three decades. It is now considerably larger than the racial achievement gaps. • Recent evidence suggests that racial disparities in high school graduation rates have declined sharply in the last decade; the difference in graduation rates is now half the size it was forty years ago. • Nonetheless, black and Hispanic students are still much less likely to earn a bachelor’s degree or to enroll in a highly selective college than are white students. These gaps in high levels of educational attainment have changed little in the last few decades. • Likewise, low-income students are substantially underrepresented in selective four-year colleges. This pattern appears to be more pronounced today than it was three decades ago.
S
ixty years ago, the Supreme Court declared de jure racial segregation of schools unconstitutional. Forty-nine years ago, Congress passed the Elementary and Secondary Education Act, which was designed in part to eliminate the achievement gap between poor and non-poor children by providing additional funding to schools enrolling large proportions of lowincome students. Both of these acts, as well as many other legislative, judicial, and policy changes in the 1960s, 1970s, and later, were intended to equalize educational opportunity for students—students from low-income families and black students—who historically had little access to high quality schools. The success of these and related efforts has been mixed. The goal in this brief is to summarize the trends in educational equity over the last several decades. I consider educational equity in relation to race and ethnicity and family income. Certainly, these are not the only relevant dimensions for a discussion of educational equity, but because they link characteristics of a child’s family to his or her educational success, they are particularly relevant for a better understanding of social mobility. In principle, it is useful to consider two types of measures of educational equity: first, measures of educational opportunity and experiences, such as school quality, access to high quality teaching, and rigorous curricula, and second, measures of educational outcomes, such as performance on standardized tests, high school graduation, college enrollment and completion. The for-
mer is more difficult to measure, because historically we haven’t collected systematic data on the quality of education children receive. As rough proxies for equality of educational opportunity, researchers typically look at patterns of segregation, school funding, and pupil-teacher ratios, but these are far from ideal measures and are generally only weakly related to educational outcomes. Because a full discussion of the complexities involved in measuring equality of educational experiences is beyond the scope of this brief, I will focus my attention here on measures of equality of educational outcomes, including academic achievement, high school graduation, and college enrollment. Trends in Academic Achievement Gaps One of the success stories in U.S. education is the substantial narrowing of racial achievement gaps over the last four decades. In the early 1970s, when the first National Assessment of Educational Progress (NAEP) tests (now known as “the Nation’s Report Card”) were administered, the white-black achievement gap in reading was well over one and a quarter standard deviations. That same gap today is half that size (see Figure 1). The same long-term trend is evident in mathematics as well, and in the white-Hispanic gaps in both math and reading. On the whole, racial achievement gaps have narrowed by roughly 40 percent over the last four decades. Nonetheless, our progress in narrowing racial achievement gaps has been uneven, and the gaps are still quite large, despite this progress. Most of the reduction in racial achievement gaps occurred for cohorts born between the 1950s and 1970s. Math gaps
54
education Trends in Racial and Income Achievement Gaps, by Birth Cohort
figure 1.
Reading
Achievement Gap (standard deviations)
were no smaller for children born in the early 1990s than they were for children born 20 years earlier; reading gaps narrowed only modestly over this same time period. More recently, however, the gaps have begun to narrow again. This recent trend is evident in the Long-Term Trend NAEP data shown in Figure 1 as well as in the so-called “Main NAEP” tests, a newer version of the NAEP tests that has been administered since 1990 and in state accountability tests.1 Both white-black and white-Hispanic gaps have narrowed by roughly two-tenths of a standard deviation in the last two decades. While it is unclear whether this trend will continue, it is certainly good news.
Trends in Racial and Income Achievement Gaps, by Birth Cohort
Math
1.50
1.50
1.25
1.25
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25
White-Black Gap
White-Black Gap
White-Hispanic Gap
White-Hispanic Gap
90-10 Income Gap
0.00 1940
1950
1960
1970
1980
Birth Year
90-10 Income Gap
0.00 1990
2000
2010
1940
1950
1960
1970
1980
1990
Birth Year
2000
2010
Source: Updated versions of figures originally published in Reardon (2011). Gaps here are measured relative to the age- and cohortspecific national standard deviation of scores. This standard deviation has changed very little over time. Racial gaps are based on author’s calculations from Long-Term Trend NAEP (NAEP-LTT) data. The NAEP-LTT tests have been administered to nationally-representative samples 9-, 13-, and 17-year olds roughly every four years from 1971-2012. The racial gap trend shown is the fitted curve from a precision-weighted least squares regression of gaps on a cubic function of birth cohort, controlling for age. Age is centered at 13. Each gap is weighted by the inverse of its estimated sampling variance. The income gap trend shown is based on a precisionweighted fitted quartic trend of estimated income achievement gaps from author’s calculations from 13 nationally representative studies from 1960-2010.
figure 2.
White-Black Achievement Gaps and Trends, by State
Average White-Black Gap, by State, 1990-2009 K cohorts, Math and Reading Pooled
Average White-Black Annual Gap Change, by State, 1990-2009 K cohorts, Math and Reading Pooled
MT WV WY HI ID
DC
VT ND NH OR ME NM KY AK WA OK UT SD AZ NV VA IN GA TN NE AL IA MO TX NY KS CO RI LA MS CA DE FL SC MD AR MA NC OH MI NJ PA MN IL CT 95% Confidence WI Interval
0.00
0.25
0.50
0.75
1.00
1.25
Average Gap
MS PA
NY IL WV OK LA AR MN RI
TX MI FL MO NJ GA MD TN VA CA AZ DE WI NM MA CO AL CT SC NC IN KY OH KS HI WA NV
95% Confidence Interval
DC
1.50
1.75
2.00
-0.06
-0.04
-0.02
0.00
SD
WY MT UT AK ME ND OR IA NH
0.02
ID
NE VT
0.04
Average Annual Gap Change
Source: Author’s calculations from Main NAEP data and state accountability test data collected from state Departments of Education and EdFacts. Estimates shown here are computed by first estimating achievement gaps in each state by year, grade, test subject, and test (NAEP or state accountability test), and then using a precision-weighted random coefficients regression model to estimate the average of these gaps, and their annual trend, in each state, adjusting for grade, subject, and test source. Gap estimates shown are empirical Bayes estimates. 95% confidence intervals are computed using the estimated posterior variance of each state’s estimate.
Progress in narrowing achievement gaps is also uneven across the country. In some states the white-black achievement gap is more than a standard deviation. In Washington, D.C., the gap is nearly 2 standard deviations); in others it is half that large (see left panel of Figure 2). Moreover, there is considerable variation across states in the trend in achievement gaps. Although the white-black achievement gap has been narrowing on average at a rate of roughly one onehundredth of a standard deviation per year over the last two decades, in some states it has been narrowing at two to three times that rate, notably, in Washington, D.C., Mississippi, Pennsylvania, New York, and Illinois. In other states, particularly those with small black populations, the whiteblack achievement gap has actually been widening (see right panel of Figure 2). As racial achievement gaps have narrowed over the last five decades, the opposite has been true of the achievement gap between children from high- and low-income families. That gap—measured as the differ-
national report card • The Stanford Center on Poverty and Inequality
education ence in average test scores between children whose families are at the 90th and 10th percentiles of the family income distributionâ&#x20AC;&#x201D;grew by 40 percent across cohorts born in the early 1970s and late 1990s (see Figure 1). The income achievement gap, which was smaller than the white-black gap for cohorts born in the 1950s and 1960s, is now considerably larger than both the white-black and white-Hispanic gaps. One of the key questions regarding the narrowing of the racial achievement gaps and the widening of the income achievement gap is whether these trends are due to changes in the quality of schools available to children. Likewise, are differences among states in the size and trends in their achievement gaps due to differences in statesâ&#x20AC;&#x2122; educational systems? Or are they due to differences in the out-of school conditions in which children grow up, such as differences in segregation patterns, income inequality, and racial socioeconomic disparities? The answers to these questions are not yet clear, but there is some research which may shed light on them.
55
income disparities, educational disparities, and segregation are smaller. All of these patterns suggest that out-of-school factors play a sizeable role in shaping achievement gaps. That said, it is not clear whether changes in out-of-school factors are the primary cause of the changes in achievement gaps. The narrowing of the racial achievement gaps coincides with the onset of the accountability movement in education, most clearly institutionalized in the No Child Left Behind (NCLB) Act of 2002, which required states and schools to explicitly attend to racial achievement gaps. Nonetheless, my research has shown that NCLB had little or no impact on racial achievement gaps. Another piece of evidence relevant here is the trend in achievement gaps when children enter kindergarten. Recent evidence comparing racial and income achievement gaps at kindergarten entry between 1998 and 2010 shows that both these gaps have narrowed over the decade (see Figure 3). The racial gaps have narrowed at a rate of about 0.07 to 0.11 standard deviations per decade over this time period, roughly the same rate as the racial gaps in elementary and middle school. This suggests that most of the narrowing of the racial gaps evident in NAEP may be due to pre-kindergarten trends, rather than improvements in educational equity during the K-12 years.
Achievement Gap (Standard Deviations)
Achievement Gap (Standard Deviations)
First, the best evidence currently available, from longitudinal studies of children as they progress through school, indicates that achievement gaps change relatively little after elementary school. Income achievement gaps are very large when children enter kindergarten Trends in Achievement Gaps at Kindergarten Entry (roughly 1.25 standard deviations) and grow by only 10 figure 3. Trends in Achievement Gaps at Kindergarten Entry percent through 8th grade, for example. The white-black Math Gap Reading Gap achievement gap does grow 1.4 1.4 somewhat in early elementary school, but is largely stable 1.2 1.2 * after that. Second, both racial ** and economic achievement 1.0 1.0 gaps appear to narrow during the months when children 0.8 0.8 are in school, and then widen again during the summer ** 0.6 0.6 months. Third, a large propor+ tion of the variance in racial 0.4 0.4 achievement gaps among states can be explained by between-state differences 0.2 90-10 Income Gap 0.2 90-10 Income Gap White-Black Gap in racial socioeconomic disWhite-Black Gap White-Hispanic Gap parities. States where the 0.0 0.0 white-black income and 1998 2006 2010 1998 2006 2010 Fall of Kindergarten Year Fall of Kindergarten Year parental education gaps are larger and where segregaNote: Stars (*) indicate change from 1998 to 2010 cohort is significant (+ p<.10; * p<.05; ** p<.01). tion is higher have much Note: Stars (*) indicate change from 1998 to 2010 cohort is significant (+ p<.10; * p<.05; ** p<.01). Source: Reardon and Portilla (2013). Estimates are based on data from the three Early Childhood Longitudinal Studies (ECLS; www.nces.ed.gov/ larger white-black achieveecls). 90-10 income gap is the estimated difference in test scores between children from families at the 90th and 10th percentiles of the family ment gaps than states where income distribution. White-Hispanic reading gap trends are not shown because of changes in the reading test format for non-native English speakers.
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education
The recent narrowing of the income gaps, evident in Figures 1 and 3, stands in contrast to the trend over the prior 25 years. While certainly a promising sign, the evidence for this reversal is based largely on the gap measured among kindergarteners in the Fall of 2010. It is too soon to tell whether this heralds the beginning of a sustained improvement in educational equity or simply reflects an anomaly in the data. Trends and Patterns of Educational Attainment Another way to gauge our success at improving educational equity is to examine recent trends in high school graduation and college completion rates (see Figures 4 and 5). For a long time in the U.S., high school graduation rates were stagnant, or even declining. Among the cohort scheduled to graduate from high school in the mid-1960s (those born in 1946-1950), roughly 81 percent earned a high school diploma. Among those born 30 years later and scheduled to graduate in the mid-1990s, only 78 percent earned a diploma. Graduation rates have been rising rapidly, however, since the mid-1990s. Indeed, the cohort that was scheduled to graduate in the mid-2000s had an 84 percent completion rate, six percentage points higher than their peers born 10 years earlier. This sharp rise in graduation rates is striking, but perhaps more striking is the fact that it is disproportionately due to rising graduation rates among black and Hispanic students. In fact, the black graduation rate rose 10 percentage points in the last decade, twice as fast as the white rate; the Hispanic rate rose 14 percentage points, three times the white rate.
U.S. High School Graduation Rate for 20-24-Year-Olds, by Race/Ethnicity and U.S.Birth High Cohort School Graduation Rate for 20–24-Year-Olds,
figure 4.
Despite the improvement in high school completion rates and the narrowing of racial graduation rate differences, there are still large disparities in patterns of educational attainment. Only 15 percent of Hispanic and 23 percent of black young adults (aged 25-29) in 2012 had a bachelor’s degree, compared with 40 percent of white young adults (see Figure 5). Moreover, the college completion rate among whites has grown more rapidly than that among blacks and Hispanics over the last four decades. Comparable trend data are not readily available by social class background. Although black and Hispanic students are increasingly likely to graduate from high school and to enroll in college, they are very disproportionately overrepresented in community colleges and non-selective four-year colleges. This is likely part of the reason why the racial/ethnic gaps in bachelor’s degree completion have not narrowed even has high school graduation gaps have narrowed. (Another reason may be that the cohorts for whom high school graduation rates have increased are still too young to be observed in the college completion data.) Figure 6 shows that roughly 35 percent of those enrolling in community college or non-selective four-year colleges are black or Hispanic, while fewer than 5 percent of those enrolling in the most selective colleges are black or Hispanic. The same pattern is evident by family income as well (see Figure 7). Students from low-income families are dramatically underrepresented in selective four-year colleges. Only 6 percent of students at the most selective colleges and univer-
figure 5. Proportion U.S. 25-29-Year-Olds WithaatBachelor's Least aDegree, Bachelor’s Proportion of of U.S. 25-29-Year-Olds With at Least Race/Ethnicity and Birth Cohort Degree, by Race/Ethnicitybyand Birth Cohort
by Race/Ethnicity and Birth Cohort
40%
Proportion with a Bachelor's Degree
High School Graduation Rate
100%
80%
60%
40% Total
20%
White Black Hispanic
0% 1950
1960
1970
1980
1990
Approximate Birth Year
Source: Murnane (2013). The high school graduation rates include only individuals who have received a conventional high school diploma (GED recipients are not counted as high school graduates here).
Total White Black Hispanic
30%
20%
10%
0% 1940
1950
1960
1970
1980
1990
Approximate Birth Year
Source: Child Trends (2013). Note that the Office of Management and Budget race definitions were changed beginning with data collected in 2003. Estimates for 25-29-year-old black young adults prior to 2003 (those born roughly prior to 1976) are for the category “non-Hispanic Black”; estimates for later cohorts are for the category “black alone.” The latter includes only individuals who identify as black and not any other race. The two category definitions are not strictly comparable.
national report card • The Stanford Center on Poverty and Inequality
education
57
Racial Composition of Postsecondary Destinations, Class of 2004 figure 6.
Racial Composition of Postsecondary Destinations, Class of 2004 Population Proportions
100%
Enrollment Composition
80%
White 60%
40%
Asian Other
Nat. American
Hispanic 20%
Black 0% Less than high school
HS graduate, not in college
Enrolled in less than 4-year college
6
5
4 3 Enrolled in 4-Year College
2
1
(by Barron's ranking: 1 = most competitive)
Educational Enrollment Status
Source: Reardon, Baker, and Klasik (2012). Data are from the Education Longitudinal Survey (ELS), a study of a nationally-representative sample of students enrolled in 10th grade in Spring 2002. The figure shows the highest postsecondary enrollment status as of Spring 2010. The width of the bars is proportional to the share of the population with each enrollment status. Four-year colleges are categorized by their Barron’s selectivity ranking.
Income Composition of Postsecondary Destinations, Class of 2004 figure 7.
Income Comparison of Postsecondary Destinations, Class of 2004 Population Proportions
100%
>$75,000
Enrollment Composition
80%
$50-75,000
60%
$35-50,000
40%
$25-35,000 20%
<$25,000
0% Less than high school
HS graduate, not in college
Enrolled in less than 4-year college
6
5
4 3 Enrolled in 4-Year College
2
1
(by Barron's ranking: 1 = most competitive)
Educational Enrollment Status
national report card • The Stanford Center on Poverty and Inequality
Source: Reardon, Baker, and Klasik (2012). Data are from the Education Longitudinal Survey (ELS), a study of a nationally-representative sample of students enrolled in 10th grade in Spring 2002. The figure shows the highest postsecondary enrollment status as of Spring 2010. The width of the bars is proportional to the share of the population with each enrollment status. Family income (2001 annual income, in 2001 dollars) was reported by parents in 2002, when the students were in 10th grade. Fouryear colleges are categorized by their Barron’s selectivity ranking.
58
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sities come from families in the bottom quintile of the income distribution. Almost 80 percent of students in these colleges come from families in the upper half of the income distribution. Some research indicates that low-income students are even more underrepresented in selective colleges now than they were three decades ago. The patterns in Figures 4 through 7 are partly the result of the achievement patterns evident in Figure 1. It is likely that part of the reason for the sharp reduction in the white-black and white-Hispanic high school graduation gaps over the last decade is the decline in racial achievement gaps. Black and Hispanic students’ math and reading skills at the beginning of high school are markedly higher than they were 20 years ago, which means they are entering high school much better prepared to succeed academically. Conversely, the fact that achievement gaps remain large— despite some recent progress—is certainly part of the reason for the disparities in bachelor’s degree attainment and enrollment at selective colleges evident in Figures 5 through 7. This conclusion is suggested by the importance of standardized test scores in admission to such colleges. Nonetheless, there are many other factors that shape college enrollment patterns, including affirmative action policies (or their absence) and trends in the cost and availability of financial aid. Recent research suggests that many high-achieving low-income students do not apply to highly selective colleges, despite having test scores that would make them eligible, perhaps because of perceptions of the cost of such colleges, lack of information about financial aid, or concerns that they would not fit in.
Conclusion The primary impression one gets from reviewing the evidence here is that inequality of educational outcomes, by race and by social class background, remains very high in the United States. That is not to say that we have not made some progress since the 1950s and 1960s. Indeed, racial disparities in academic achievement and high school graduation are smaller and, in the case of achievement gaps, substantially smaller, than they were 40 years ago. And in most states, these racial disparities continue to narrow, albeit slowly in most places. We have been less successful, however, at reducing disparities in the highest levels of academic attainment: black and Hispanic students obtain bachelor’s degrees at rates far below those of whites, and are dramatically underrepresented in the most selective four-year colleges and universities. Progress in narrowing socioeconomic disparities in educational outcomes, however, has been even more elusive than racial progress. In fact, socioeconomic gaps in academic achievement have widened substantially in the recent decades. The one bright spot of evidence here, however, is the indication in very recent data that socioeconomic gaps in kindergarten readiness have narrowed in the last decade, perhaps presaging an era of progress and reduced inequality. But such progress, and continued progress in narrowing racial disparities, will not occur without focused policy attention on improving both our schools and the wide economic disparities that inhibit the educational success of the nation’s children. ■
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Notes 1. See, for example, Hemphill, Vanneman, and Rahman, 2011; Vanneman, Hamilton, Baldwin Anderson, and Rahman, 2009; and Reardon, Valentino, Kalogrides, Shores, and Greenberg, 2013.
Additional Resources Child Trends. 2013. “Educational Attainment.” www.childtrends.org/?indicators=educationalattainment
Murnane, Richard J. 2013. “U.S. High School Graduation Rates: Patterns and Explanations.” Journal of Economic Literature, 51(2), 370-422.
Hemphill, F. Cadelle, Alan Vanneman, and Taslima Rahman. 2011. “Achievement Gaps: How Hispanic and White Students in Public Schools Perform in Mathematics and Reading on the National Assessment of Educational Progress”. Washington, D.C.: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.
Reardon, Sean. F., Rachel Baker, and Daniel Klasik. 2012. “Race, Income, and Enrollment Patterns in Highly Selective Colleges, 19822004.” Stanford, CA: Center for Education Policy Analysis, Stanford University.
Reardon, Sean F., Rachel A. Valentino, Demetra Kalogrides, Kenneth A. Shores, and Erica H. Greenberg. 2013. “Patterns and Trends in Racial Academic Achievement Gaps Among States, 1999-2011.” Working Paper. Stanford, CA: Center for Education Policy Analysis, Stanford University. Vanneman, Alan, Linda Hamilton, Janet Baldwin Anderson, and Taslima Rahman. 2009. “Achievement Gaps: How Black and White Students in Public Schools Perform in Mathematics and Reading on the National Assessment of Educational Progress.” Washington, D.C.: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.
national report card • The Stanford Center on Poverty and Inequality
The Stanford Center on Poverty and Inequality The Stanford Center on Poverty and Inequality monitors and publicizes trends in poverty and inequality, publishes the countryâ&#x20AC;&#x2122;s leading magazine on poverty and inequality, supports research on the causes of poverty and inequality, and examines the effects of public policy on poverty and inequality. The CPI, which is part of the Institute for Research in the Social Sciences, is generously supported by the Elfenworks Foundation and Stanford University. Partial funding for this research came from a grant to the Stanford Center on Poverty and Inequality from The Russell Sage Foundation. The United Way of the Bay Area also generously supported this initiative. The Stanford Center on Poverty and Inequality is funded by Grant Number AE00101 from the U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, awarded by the Substance Abuse Mental Health Service Administration. The contents of this report are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Health and Human Services (Office of the Assistant Secretary for Planning and Evaluation) or the Substance Abuse Mental Health Service Administration.
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About The Author John C (Jack) Johnson III Founder & CEO
Having become disillusioned with the inner-workings of the ―Cradle-to-Prison‖ pipeline, former practicing attorney Johnson set out, in 2001, to try to help usher-in fundamental changes in the area of Juvenile and Transformative Justice. Educated at Temple University, in Philadelphia, Pennsylvania and Rutgers Law School, in Camden, New Jersey, Jack moved to Atlanta, Georgia to pursue greater opportunities to provide Advocacy and Preventive Programmatic services for at-risk/ atpromise young persons, their families, and Justice Professionals embedded in the Juvenile Justice process in order to help facilitate its transcendence into the 21st Century. There, along with a small group of community and faith-based professionals, ―The Advocacy Foundation, Inc." was conceived and implemented over roughly a thirteen year period, originally chartered as a Juvenile Delinquency Prevention and Educational Support Services organization consisting of Mentoring, Tutoring, Counseling, Character Development, Community Change Management, Practitioner Re-Education & Training, and a host of related components. The Foundation‘s Overarching Mission is “To help Individuals, Organizations, & Communities Achieve Their Full Potential”, by implementing a wide array of evidence-based proactive multi-disciplinary "Restorative & Transformative Justice" programs & projects currently throughout the northeast, southeast, and western international-waters regions, providing prevention and support services to at-risk/ at-promise youth, to young adults, to their families, and to Social Service, Justice and Mental Health professionals‖ everywhere. The Foundation has since relocated its headquarters to Philadelphia, Pennsylvania, and been expanded to include a three-tier mission. In addition to his work with the Foundation, Jack also served as an Adjunct Professor of Law & Business at National-Louis University of Atlanta (where he taught Political Science, Business & Legal Ethics, Labor & Employment Relations, and Critical Thinking courses to undergraduate and graduate level students). Jack has also served as Board President for a host of wellestablished and up & coming nonprofit organizations throughout the region, including ―Visions Unlimited Community Development Systems, Inc.‖, a multi-million dollar, award-winning, Violence Prevention and Gang Intervention Social Service organization in Atlanta, as well as Vice-Chair of the Georgia/ Metropolitan Atlanta Violence Prevention Partnership, a state-wide 300 organizational member violence prevention group led by the Morehouse School of Medicine, Emory University and The Atlanta-Based Martin Luther King Center. Attorney Johnson‘s prior accomplishments include a wide-array of Professional Legal practice areas, including Private Firm, Corporate and Government postings, just about all of which yielded significant professional awards & accolades, the history and chronology of which are available for review online.
www.TheAdvocacyFoundation.org Clayton County Youth Services Partnership, Inc. – Chair; Georgia Violence Prevention Partnership, Inc – Vice Chair; Fayette County NAACP - Legal Redress Committee Chairman; Clayton County Fatherhood Initiative Partnership – Principal Investigator; Morehouse School of Medicine School of Community Health Feasibility Study - Steering Committee; Atlanta Violence Prevention Capacity Building Project – Project Partner; Clayton County Minister‘s Conference, President 2006-2007; Liberty In Life Ministries, Inc. – Board Secretary; Young Adults Talk, Inc. – Board of Directors; ROYAL, Inc - Board of Directors; Temple University Alumni Association; Rutgers Law School Alumni Association; Sertoma International; Our Common Welfare Board of Directors – President)2003-2005; River‘s Edge Elementary School PTA (Co-President); Summerhill Community Ministries; Outstanding Young Men of America; Employee of the Year; Academic All-American - Basketball; Church Trustee.
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www.TheAdvocacyFoundation.org
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